Research_diary
colby_woodson
1/23/2023
library(ggplot2)
library(sf)
library(dplyr)
library(tidyverse)
library(tidycensus)
library(tigris)
library(tidyr)
options(tigris_use_cache = TRUE)What is an “LCSA,” why does it matter and how was it made? A combined statistical area is two adjacent metropolitan or micropolitan statistical areas that have an employment interchange measure (EIM) of at least 15. This research coins the phrase ‘large combined statistical area’ or LCSA, defined as only those CSAs that contain a metropolitan area. The LSCA is used here as a way to narrow the researcher’s scale to an area that contains a population that is engaged in dynamic inter-county activity, such as concievably relocating to be closer to work or to buy a home for a new family. It is thought that this is an insightful way to learn more about housing costs over the 2010s. However, as the boundaries of a CSA ‘float’ every three to five years, customized LCSAs that contain all the counties that were ever part of an LCSA from 2010 through 2019 were made and statistics derived from county estimates were calculated.
mapping_example <- map_dfr(c("AL","GA","TN"),
~{counties(.x, cb = TRUE, year = 2010)})Using FIPS code '01' for state 'AL'
Using FIPS code '13' for state 'GA'
Using FIPS code '47' for state 'TN'mapping_example_CSA_09to13 <- combined_statistical_areas(cb = FALSE, year = 2011) %>%
filter(str_detect(NAME, "AL") | str_detect(NAME, "GA") | str_detect(NAME, "TN")) %>%
filter(NAMELSAD == "Chattanooga-Cleveland-Athens, TN-GA CSA")
mapping_example_muni_09to13 <- core_based_statistical_areas(year = 2011) %>%
filter(str_detect(NAME, "AL") | str_detect(NAME, "GA") | str_detect(NAME, "TN")) %>%
filter(str_detect(NAMELSAD, "Metro"))
example_09to13 <- ggplot() +
geom_sf(data = mapping_example, fill = "lightgray", color = "gray") +
geom_sf(data = mapping_example_muni_09to13, fill = "green", color = "green") +
geom_sf(data = mapping_example_CSA_09to13, fill = NA, color = "red") +
theme_void()Example from 09 to 13 ref map
example_09to13
mapping_example_CSA_13to18 <- combined_statistical_areas(cb = FALSE, year = 2015) %>%
filter(str_detect(NAME, "AL") | str_detect(NAME, "GA") | str_detect(NAME, "TN")) %>%
filter(NAMELSAD == "Chattanooga-Cleveland-Dalton, TN-GA-AL CSA")
mapping_example_muni_13to18 <- core_based_statistical_areas(year = 2015) %>%
filter(str_detect(NAME, "AL") | str_detect(NAME, "GA") | str_detect(NAME, "TN")) %>%
filter(str_detect(NAMELSAD, "Metro"))
example_13to18 <- ggplot() +
geom_sf(data = mapping_example, fill = "lightgray", color = "gray") +
geom_sf(data = mapping_example_muni_13to18, fill = "green", color = "green") +
geom_sf(data = mapping_example_CSA_13to18, fill = NA, color = "red") +
theme_void()Example from 13 to 18 ref map
example_13to18
mapping_example_CSA_18to20 <- combined_statistical_areas(cb = FALSE, year = 2019) %>%
filter(str_detect(NAME, "AL") | str_detect(NAME, "GA") | str_detect(NAME, "TN")) %>%
filter(NAMELSAD == "Chattanooga-Cleveland-Dalton, TN-GA CSA" |
NAMELSAD == "Scottsboro-Fort Payne, AL CSA")
mapping_example_muni_18to20 <- core_based_statistical_areas(year = 2019) %>%
filter(str_detect(NAME, "AL") | str_detect(NAME, "GA") | str_detect(NAME, "TN")) %>%
filter(str_detect(NAMELSAD, "Metro"))
example_18to20 <- ggplot() +
geom_sf(data = mapping_example, fill = "lightgray", color = "gray") +
geom_sf(data = mapping_example_muni_18to20, fill = "green", color = "green") +
geom_sf(data = mapping_example_CSA_18to20, fill = NA, color = "red") +
theme_void()Example from 18 to 20 ref map
example_18to20
customized <- read_csv("LCSA_cut.csv")Missing column names filled in: 'X1' [1]
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────
cols(
X1 = col_double(),
year = col_double(),
GEOID = col_character(),
NAME = col_character(),
variable = col_character(),
estimate = col_double(),
moe = col_double(),
NAMELSAD = col_character()
)cut <- customized$GEOID
mapping_example_pre_cut <- mapping_example %>% select(GEOID = GEO_ID, geometry)
mapping_example_cut <- mapping_example_pre_cut %>% separate(GEOID, c("nope", "GEOID"), "US") %>%
select(-"nope")
mapping_example2 <- filter(mapping_example_cut, GEOID %in% cut)
mapping_example3 <- geo_join(mapping_example2, customized, by = c("GEOID")) %>%
filter(., GEOID %in% cut)
mapping_example4 <- mapping_example3 %>% mutate(group = case_when(TRUE ~ NAMELSAD))
mapping_example4 <- group_by(mapping_example4, group) %>%
summarize(do_union = TRUE) %>% filter(!is.na(.)) %>%
filter(group == "Chattanooga-Cleveland-Dalton, TN-GA-AL CSA")`summarise()` ungrouping output (override with `.groups` argument)customized_example2 <- ggplot() +
geom_sf(data = mapping_example, fill = "lightgray", color = "gray") +
geom_sf(data = mapping_example4, fill = "red", color = NA) +
theme_void()Custom 2010s LCSA
customized_example2
costs <- customized %>%
pivot_wider(., id_cols = c("year", "GEOID", "NAME", "NAMELSAD"),
names_from = variable, values_from = c("estimate", "moe")) %>%
select(year, GEOID, NAME, NAMELSAD,
RentCost = estimate_B25071_001, RentCostMOE = moe_B25071_001,
MortgagorCost = estimate_B25092_002, MortgagorCostMOE = moe_B25092_002,
OwnerCost = estimate_B25092_003, OwnerCostMOE = moe_B25092_003,
MedianMortAndOwnCost = estimate_B25092_001, MedianMortAndOwnMOE = moe_B25092_001,
Renters = estimate_B25003_003, RentersMOE = moe_B25003_003,
Mortgagors = estimate_B25081_002, MortgagorsMOE = moe_B25081_002,
Owners = estimate_B25081_008, OwnersMOE = moe_B25081_008,
MortAndOwn = estimate_B25003_002, MortAndOwnMOE = moe_B25003_002,
OccupiedTotal = estimate_B25003_001, OccupiedTotalMOE = moe_B25003_001,
MedianHousingCost = estimate_B25105_001, MedianHousingCostMOE = moe_B25105_001) %>%
filter(!is.na(NAMELSAD))
costs2 <- costs %>% select(year, GEOID, NAME, NAMELSAD, RentCost, MortgagorCost, OwnerCost, MedianMortAndOwnCost,
Renters, Mortgagors, Owners, MortAndOwn, OccupiedTotal, MedianHousingCost)
head <- costs2 %>% filter(is.na(MortgagorCost))Recoding zeros for mortgagor costs due to the county essentially containing no mortgagors, this recoding only pertains to Kenedy County
head(head)index <- is.na(costs2)
costs2[index] <- 0
head <- costs2 %>% filter(str_detect(NAME, "Kenedy County, Texas"))head(head)“CostFirst” weights resident cohorts’ costs by their prevelance; differientiates between renters, mortgagors and owners, I’m very partial to this method over the “CostSecond” method that uses the variable with the mortgagors and owners lumped together
costs3 <- costs2 %>%
select(year, GEOID, NAME, NAMELSAD, RentCost, MortgagorCost, OwnerCost, MedianMortAndOwnCost,
Renters, Mortgagors, Owners, MortAndOwn, OccupiedTotal, MedianHousingCost) %>%
mutate(RentersOverTotal = Renters /OccupiedTotal) %>%
mutate(MortgagorsOverTotal = Mortgagors /OccupiedTotal) %>%
mutate(OwnerOverTotal = Owners /OccupiedTotal) %>%
mutate(MortAndOwnOverTotal = MortAndOwn /OccupiedTotal) %>%
mutate(CostFirst = RentersOverTotal*RentCost + MortgagorsOverTotal*MortgagorCost + OwnerOverTotal*OwnerCost) %>%
mutate(CostSecond = RentersOverTotal*RentCost + MortAndOwnOverTotal*MedianMortAndOwnCost) %>%
mutate(FirstTotal = RentersOverTotal + MortgagorsOverTotal + OwnerOverTotal) %>%
mutate(SecondTotal = RentersOverTotal + MortAndOwnOverTotal) %>%
pivot_wider(id_cols = c("GEOID", "NAME", "NAMELSAD"),
names_from = year,
values_from = c("RentCost", "MortgagorCost", "OwnerCost",
"MedianMortAndOwnCost", "Renters", "Mortgagors",
"Owners", "MortAndOwn", "OccupiedTotal",
"RentersOverTotal", "MortgagorsOverTotal", "OwnerOverTotal",
"MortAndOwnOverTotal", "CostFirst", "CostSecond",
"MedianHousingCost", "FirstTotal", "SecondTotal")) %>%
mutate(PointChangeFirst = CostFirst_2019 - CostFirst_2014) %>%
mutate(PointChangeSecond = CostSecond_2019 - CostSecond_2014)head(costs3)Bringing the un-recoded MOEs back into the dataframe
costs4 <- costs %>%
select(year, GEOID, NAME, NAMELSAD, RentCostMOE, MortgagorCostMOE, OwnerCostMOE,
MedianMortAndOwnMOE, RentersMOE, MortgagorsMOE, OwnersMOE, MortAndOwnMOE,
OccupiedTotalMOE, MedianHousingCostMOE) %>%
pivot_wider(id_cols = c("GEOID", "NAME", "NAMELSAD"),
names_from = year,
values_from = c("RentCostMOE", "MortgagorCostMOE", "OwnerCostMOE",
"MedianMortAndOwnMOE", "RentersMOE", "MortgagorsMOE",
"OwnersMOE", "MortAndOwnMOE", "OccupiedTotalMOE",
"MedianHousingCostMOE")) %>%
full_join(., costs3, by = c("GEOID", "NAME", "NAMELSAD"))
cut2 <- costs4$GEOIDhead(costs4)SHP <- map_dfr(c("WA", "OR", "NV", "CA", "ID", "AZ", "UT",
"CO", "NM", "TX", "OK", "KS", "NE", "SD",
"ND", "MN", "IA", "MO", "AR", "LA", "WI",
"IL", "KY", "TN", "MS", "FL", "AL", "GA",
"SC", "NC", "VA", "WV", "OH", "MI", "NY",
"PA", "VT", "NH", "ME", "MA", "RI", "CT",
"NJ", "DE", "MD", "DC", "IN"
), ~{
counties(.x, cb = TRUE, year = 2014)})Using FIPS code '53' for state 'WA'
Using FIPS code '41' for state 'OR'
Using FIPS code '32' for state 'NV'
Using FIPS code '06' for state 'CA'
Using FIPS code '16' for state 'ID'
Using FIPS code '04' for state 'AZ'
Using FIPS code '49' for state 'UT'
Using FIPS code '08' for state 'CO'
Using FIPS code '35' for state 'NM'
Using FIPS code '48' for state 'TX'
Using FIPS code '40' for state 'OK'
Using FIPS code '20' for state 'KS'
Using FIPS code '31' for state 'NE'
Using FIPS code '46' for state 'SD'
Using FIPS code '38' for state 'ND'
Using FIPS code '27' for state 'MN'
Using FIPS code '19' for state 'IA'
Using FIPS code '29' for state 'MO'
Using FIPS code '05' for state 'AR'
Using FIPS code '22' for state 'LA'
Using FIPS code '55' for state 'WI'
Using FIPS code '17' for state 'IL'
Using FIPS code '21' for state 'KY'
Using FIPS code '47' for state 'TN'
Using FIPS code '28' for state 'MS'
Using FIPS code '12' for state 'FL'
Using FIPS code '01' for state 'AL'
Using FIPS code '13' for state 'GA'
Using FIPS code '45' for state 'SC'
Using FIPS code '37' for state 'NC'
Using FIPS code '51' for state 'VA'
Using FIPS code '54' for state 'WV'
Using FIPS code '39' for state 'OH'
Using FIPS code '26' for state 'MI'
Using FIPS code '36' for state 'NY'
Using FIPS code '42' for state 'PA'
Using FIPS code '50' for state 'VT'
Using FIPS code '33' for state 'NH'
Using FIPS code '23' for state 'ME'
Using FIPS code '25' for state 'MA'
Using FIPS code '44' for state 'RI'
Using FIPS code '09' for state 'CT'
Using FIPS code '34' for state 'NJ'
Using FIPS code '10' for state 'DE'
Using FIPS code '24' for state 'MD'
Using FIPS code '11' for state 'DC'
Using FIPS code '18' for state 'IN'SHP2 <- filter(SHP, GEOID %in% cut2)
SHP3 <- geo_join(SHP2, costs4, by = c("GEOID"))
SHP4 <- SHP3 %>% mutate(group = case_when(TRUE ~ NAMELSAD))
SHP5 <- group_by(SHP4, group) %>%
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st_as_s2(): dropping Z and/or M coordinateCostsMap <- ggplot() +
geom_sf(data = SHP4, aes(fill = PointChangeFirst), color = NA) +
labs(title = "percentage point change in costs over income through the 2010s",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
theme_void() +
scale_fill_binned(limits = c(-6.00,6.00), high = "purple", low = "yellow",
breaks = c(-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))CostsMap_2014 <- ggplot() +
geom_sf(data = SHP4, aes(fill = CostFirst_2014), color = NA) +
labs(title = "costs over income 2014",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
theme_void() +
scale_fill_binned(limits = c(0,40), high = "purple", low = "yellow",
breaks = c(12, 18, 24, 30, 36)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))CostsMap_2019 <- ggplot() +
geom_sf(data = SHP4, aes(fill = CostFirst_2019), color = NA) +
labs(title = "costs over income 2019",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
theme_void() +
scale_fill_binned(limits = c(0,40), high = "purple", low = "yellow",
breaks = c(12, 18, 24, 30, 36)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))CostsMap_2014
CostsMap_2019
CostsMap
Aggrigating to the LCSA by weighting resident cohorts’ cost contribution to the LCSA by their county cohorts in the numerator and the total of LCSA occupied units in the demoninator
costs5 <- costs4 %>% mutate(group = case_when(TRUE ~ NAMELSAD))
costs6 <- group_by(costs5, group) %>%
mutate(CSAoccupiedTotal_2014 = sum(OccupiedTotal_2014),
do_union = TRUE) %>%
mutate(CSAoccupiedTotal_2019 = sum(OccupiedTotal_2019),
do_union = TRUE) %>%
mutate(CSArenters_2014 = sum(Renters_2014),
do_union = TRUE) %>%
mutate(CSArenters_2019 = sum(Renters_2019),
do_union = TRUE) %>%
mutate(CSAmortgagors_2014 = sum(Mortgagors_2014),
do_union = TRUE) %>%
mutate(CSAmortgagors_2019 = sum(Mortgagors_2019),
do_union = TRUE) %>%
mutate(CSAowners_2014 = sum(Owners_2014),
do_union = TRUE) %>%
mutate(CSAowners_2019 = sum(Owners_2019),
do_union = TRUE) %>%
mutate(CSAmortANDown_2014 = sum(MortAndOwn_2014),
do_union = TRUE) %>%
mutate(CSAmortANDown_2019 = sum(MortAndOwn_2019),
do_union = TRUE) %>%
mutate(CSArentersOverTotal_2014 = Renters_2014 /CSAoccupiedTotal_2014) %>%
mutate(CSArentersOverTotal_2019 = Renters_2019 /CSAoccupiedTotal_2019) %>%
mutate(CSAmortgagorsOverTotal_2014 = Mortgagors_2014/CSAoccupiedTotal_2014) %>%
mutate(CSAmortgagorsOverTotal_2019 = Mortgagors_2019/CSAoccupiedTotal_2019) %>%
mutate(CSAownersOverTotal_2014 = Owners_2014 /CSAoccupiedTotal_2014) %>%
mutate(CSAownersOverTotal_2019 = Owners_2019 /CSAoccupiedTotal_2019) %>%
mutate(CSAmortANDownOverTotal_2014 = MortAndOwn_2014/CSAoccupiedTotal_2014) %>%
mutate(CSAmortANDownOverTotal_2019 = MortAndOwn_2019/CSAoccupiedTotal_2019) %>%
mutate(CSAcostFirst_2014 =
sum(CSArentersOverTotal_2014*RentCost_2014 +
CSAmortgagorsOverTotal_2014*MortgagorCost_2014 +
CSAownersOverTotal_2014*OwnerCost_2014),
do_union = TRUE) %>%
mutate(CSAcostFirst_2019 =
sum(CSArentersOverTotal_2019*RentCost_2019 +
CSAmortgagorsOverTotal_2019*MortgagorCost_2019 +
CSAownersOverTotal_2019*OwnerCost_2019),
do_union = TRUE) %>%
mutate(CSAcostSecond_2014 =
sum(CSArentersOverTotal_2014*RentCost_2014 +
CSAmortANDownOverTotal_2014*MedianMortAndOwnCost_2014),
do_union = TRUE) %>%
mutate(CSAcostSecond_2019 =
sum(CSArentersOverTotal_2019*RentCost_2019 +
CSAmortANDownOverTotal_2019*MedianMortAndOwnCost_2019),
do_union = TRUE) %>%
mutate(tot_test =
sum(CSArentersOverTotal_2014 +
CSAmortANDownOverTotal_2014),
do_union = TRUE) %>%
mutate(CSApointChangeFirst = CSAcostFirst_2019 - CSAcostFirst_2014) %>%
mutate(CSApointChangeSecond = CSAcostSecond_2019 - CSAcostSecond_2014)head(costs6)costs7 <- costs6 %>%
select(group, CSApointChangeFirst, CSAcostFirst_2019, CSAcostFirst_2014) %>%
distinct(., group, .keep_all = TRUE)
SHP6 <- geo_join(SHP5, costs7, by = c("group"))
CSA_SHP <- ggplot(data = SHP6, aes(fill = CSApointChangeFirst)) +
labs(title = "percentage point change in costs over income through the 2010s",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))CSA_SHP_2014 <- ggplot(data = SHP6, aes(fill = CSAcostFirst_2014)) +
labs(title = "LCSA costs over income 2014",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(low = "darkgreen", mid = "tan", high = "red", midpoint = 25) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))CSA_SHP_2019 <- ggplot(data = SHP6, aes(fill = CSAcostFirst_2019)) +
labs(title = "LCSA costs over income 2019",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(low = "darkgreen", mid = "tan", high = "red", midpoint = 25) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))CSA_SHP_2014
CSA_SHP_2019
CSA_SHP
Searching for any missing values for starters homes
starters <- read_csv("permits.csv")Missing column names filled in: 'X1' [1]
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────
cols(
X1 = col_double(),
year = col_double(),
GEOID = col_character(),
COUNTY = col_character(),
STATE = col_character(),
StarterHomePermits = col_double()
)head_start <- starters %>%
pivot_wider(id_cols = c("GEOID", "COUNTY"),
names_from = year,
values_from = c("StarterHomePermits")) %>%
filter(GEOID %in% cut2) %>%
select(GEOID, COUNTY, p10 = "2010", p11 = "2011", p12 = "2012", p13 = "2013",
p14 = "2014", p15 = "2015", p16 = "2016", p17 = "2017", p18 = "2018",
p19 = "2019")Values are not uniquely identified; output will contain list-cols.
* Use `values_fn = list` to suppress this warning.
* Use `values_fn = length` to identify where the duplicates arise
* Use `values_fn = {summary_fun}` to summarise duplicateshead_start$p10 <- as.numeric(as.character(head_start$p10))NAs introduced by coercionhead_start$p11 <- as.numeric(as.character(head_start$p11))NAs introduced by coercionhead_start$p12 <- as.numeric(as.character(head_start$p12))NAs introduced by coercionhead_start$p13 <- as.numeric(as.character(head_start$p13))NAs introduced by coercionhead_start$p14 <- as.numeric(as.character(head_start$p14))NAs introduced by coercionhead_start$p15 <- as.numeric(as.character(head_start$p15))NAs introduced by coercionhead_start$p16 <- as.numeric(as.character(head_start$p16))
head_start$p17 <- as.numeric(as.character(head_start$p17))
head_start$p18 <- as.numeric(as.character(head_start$p18))NAs introduced by coercionhead_start$p19 <- as.numeric(as.character(head_start$p19))NAs introduced by coercionhead_start2 <- full_join(costs6, head_start, by = "GEOID") %>%
filter(is.na(p10) | is.na(p11) | is.na(p12) | is.na(p13) |
is.na(p14) | is.na(p15) | is.na(p16) | is.na(p17) |
is.na(p18) | is.na(p19)) %>%
select(GEOID, NAME, NAMELSAD, p10, p11, p12, p13, p14, p15,
p16, p17, p18, p19)Adding missing grouping variables: `group`head(head_start2, 22)The proportional average of starterhomes over 2019’s total units will be used to generate a multiplier for the 22 missing values, this makes sense because housing development booms and busts in conjuction year over year in accordance with global macroeconomic trends in all areas in the US. That’s the reason building permits are included in so many leading economic indexes.
cut3 <- head_start2$GEOID
housing_units <- read_csv("units.csv")Missing column names filled in: 'X1' [1]
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────
cols(
X1 = col_double(),
year = col_double(),
GEOID = col_character(),
NAME = col_character(),
variable = col_character(),
estimate = col_double(),
moe = col_double()
)housing_units2 <- housing_units %>%
filter(GEOID %in% cut2)
housing_units3 <- housing_units2 %>%
pivot_wider(id_cols = c("year","GEOID", "NAME"),
names_from = variable,
values_from = c("estimate", "moe")) %>%
pivot_wider(id_cols = c("GEOID", "NAME"),
names_from = year,
values_from = c("estimate_B25002_001", "moe_B25002_001"))
housing_units4 <- housing_units3 %>%
filter(!GEOID %in% cut3) %>%
select(GEOID, NAME,
Units_2014 = estimate_B25002_001_2014,
Units_2019 = estimate_B25002_001_2019,
UnitsMOE_2014 = moe_B25002_001_2014,
UnitsMOE_2019 = moe_B25002_001_2019)
housing_units5 <- full_join(housing_units4, head_start, by = "GEOID") %>%
select(GEOID, NAME, Units_2014, Units_2019,
p10, p11, p12, p13, p14,
p15, p16, p17, p18, p19,
UnitsMOE_2014, UnitsMOE_2019) %>%
filter(!GEOID %in% cut3)
uni_tot <- sum(housing_units5$Units_2019)
p10_tot <- sum(housing_units5$p10)
p11_tot <- sum(housing_units5$p11)
p12_tot <- sum(housing_units5$p12)
p13_tot <- sum(housing_units5$p13)
p14_tot <- sum(housing_units5$p14)
p15_tot <- sum(housing_units5$p15)
p16_tot <- sum(housing_units5$p16)
p17_tot <- sum(housing_units5$p17)
p18_tot <- sum(housing_units5$p18)
p19_tot <- sum(housing_units5$p19)
weight10 <- p10_tot/uni_tot
weight11 <- p11_tot/uni_tot
weight12 <- p12_tot/uni_tot
weight13 <- p13_tot/uni_tot
weight14 <- p14_tot/uni_tot
weight15 <- p15_tot/uni_tot
weight16 <- p16_tot/uni_tot
weight17 <- p17_tot/uni_tot
weight18 <- p18_tot/uni_tot
weight19 <- p19_tot/uni_tot
housing_units6 <- housing_units3 %>% filter(GEOID %in% cut3) %>%
select(GEOID, NAME, Units_2014 = estimate_B25002_001_2014,
Units_2019 = estimate_B25002_001_2019, UnitsMOE_2014 = moe_B25002_001_2014,
UnitsMOE_2019 = moe_B25002_001_2019)
housing_units7 <- left_join(housing_units6, head_start2, by = c("GEOID", "NAME")) %>%
select(GEOID, NAME, Units_2014, Units_2019, p10, p11,
p12, p13, p14, p15, p16, p17, p18, p19, UnitsMOE_2014,
UnitsMOE_2019)head(housing_units7, 22)Using 2019’s total units for all ten years makes sense here because we’re building toward the prensent day’s total
housing_units8 <- housing_units7 %>%
mutate(p10 = case_when(is.na(p10) ~ Units_2019*weight10,
!is.na(p10) ~ p10)) %>%
mutate(p10 = round(p10*1, 0)) %>%
mutate(p11 = case_when(is.na(p11) ~ Units_2019*weight11,
!is.na(p11) ~ p11)) %>%
mutate(p11 = round(p11*1, 0)) %>%
mutate(p12 = case_when(is.na(p12) ~ Units_2019*weight12,
!is.na(p12) ~ p12)) %>%
mutate(p12 = round(p12*1, 0)) %>%
mutate(p13 = case_when(is.na(p13) ~ Units_2019*weight13,
!is.na(p13) ~ p13)) %>%
mutate(p13 = round(p13*1, 0)) %>%
mutate(p14 = case_when(is.na(p14) ~ Units_2019*weight14,
!is.na(p14) ~ p14)) %>%
mutate(p14 = round(p14*1, 0)) %>%
mutate(p15 = case_when(is.na(p15) ~ Units_2019*weight15,
!is.na(p15) ~ p15)) %>%
mutate(p15 = round(p15*1, 0)) %>%
mutate(p16 = case_when(is.na(p16) ~ Units_2019*weight16,
!is.na(p16) ~ p16)) %>%
mutate(p16 = round(p16*1, 0)) %>%
mutate(p17 = case_when(is.na(p17) ~ Units_2019*weight17,
!is.na(p17) ~ p17)) %>%
mutate(p17 = round(p17*1, 0)) %>%
mutate(p18 = case_when(is.na(p18) ~ Units_2019*weight18,
!is.na(p18) ~ p18)) %>%
mutate(p18 = round(p18*1, 0)) %>%
mutate(p19 = case_when(is.na(p19) ~ Units_2019*weight19,
!is.na(p19) ~ p19)) %>%
mutate(p19 = round(p19*1, 0))There’s no need to clap, but you can if you want to
head(housing_units8, 22)Let’s put those counties back into the main dataframe
housing_units9 <- full_join(housing_units8, housing_units5,
by = c("GEOID", "NAME", "Units_2014", "Units_2019", "p10",
"p11", "p12", "p13", "p14", "p15", "p16", "p17", "p18",
"p19", "UnitsMOE_2014", "UnitsMOE_2019")) %>%
mutate(NewStarters_2014 = p10+p11+p12+p13+p14) %>%
mutate(NewStarters_2019 = p15+p16+p17+p18+p19) %>%
mutate(StarterOverTotPP_2014 = 100*(NewStarters_2014/Units_2014)) %>%
mutate(StarterOverTotPP_2019 = 100*(NewStarters_2019/Units_2019)) %>%
mutate(StarterRatioPPchange = StarterOverTotPP_2019-StarterOverTotPP_2014) %>%
mutate(StarterRatioDecade = 100*(NewStarters_2014+NewStarters_2019)/Units_2019)
housing_units10 <- housing_units9 %>%
select("GEOID", "NAME", "Units_2014", "Units_2019","UnitsMOE_2014", "UnitsMOE_2019",
"NewStarters_2014", "NewStarters_2019", "StarterOverTotPP_2014", "StarterOverTotPP_2019",
"StarterRatioPPchange", "StarterRatioDecade")
costs8 <- full_join(housing_units10, costs6, by = c("GEOID", "NAME"))SHP7 <- SHP2 %>% select(GEOID, NAME, geometry)
SHP8 <- geo_join(SHP7, costs8, by = c("GEOID"))
lets_get_started <- ggplot() +
geom_sf(data = SHP8, aes(fill = StarterRatioPPchange), color = NA) +
labs(title = "percentage point change in new single-family unit permits over total units",
subtitle = " 2010s, LCSA counties",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
theme_void() +
scale_fill_binned(limits = c(-13,13), high = "purple", low = "yellow",
breaks = c(-10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))lets_get_started_2014 <- ggplot() +
geom_sf(data = SHP8, aes(fill = StarterOverTotPP_2014), color = NA) +
labs(title = "single-family unit permits over total units",
subtitle = " 2014, LCSA counties",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
theme_void() +
scale_fill_binned(limits = c(-10,26), high = "purple", low = "yellow",
breaks = c(0, 2, 4, 6, 8, 10, 12, 18)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))lets_get_started_2019 <- ggplot() +
geom_sf(data = SHP8, aes(fill = StarterOverTotPP_2019), color = NA) +
labs(title = "single-family unit permits over total units",
subtitle = " 2019, LCSA counties",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
theme_void() +
scale_fill_binned(limits = c(-10,26), high = "purple", low = "yellow",
breaks = c(0, 2, 4, 6, 8, 10, 12, 18)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))lets_get_started_2014
lets_get_started_2019
lets_get_started
SHP7 <- SHP2 %>% select(GEOID, NAME, geometry)
SHP8 <- geo_join(SHP7, costs8, by = c("GEOID"))
lets_get_started1 <- ggplot() +
geom_sf(data = SHP8, aes(fill = StarterRatioDecade), color = NA) +
labs(title = "percentage of total units from new single-family unit permits",
subtitle = " 2010s, LCSA counties",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
theme_void() +
scale_fill_binned(limits = c(-13,13), high = "purple", low = "yellow",
breaks = c(0, 2, 4, 6, 8, 10)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))lets_get_started1
costs8 <- group_by(costs8, group) %>%
mutate(CSAunitsTotal_2019 = sum(Units_2019),
do_union = TRUE) %>%
mutate(CSAunitsTotal_2014 = sum(Units_2014),
do_union = TRUE) %>%
mutate(CSAunitsOverTotalUnits_2014 = Units_2014/CSAunitsTotal_2014) %>%
mutate(CSAunitsOverTotalUnits_2019 = Units_2019/CSAunitsTotal_2019) %>%
mutate(CSAstarterRatioPPchange = sum(CSAunitsOverTotalUnits_2019*StarterRatioPPchange),
do_union = TRUE) %>%
mutate(CSAstarterRatioDecade = sum(CSAunitsOverTotalUnits_2019*StarterRatioDecade),
do_union = TRUE) %>%
mutate(CSAstarterOverTotPP_2014 = sum(CSAunitsOverTotalUnits_2014*StarterOverTotPP_2014),
do_union = TRUE) %>%
mutate(CSAstarterOverTotPP_2019 = sum(CSAunitsOverTotalUnits_2019*StarterOverTotPP_2019),
do_union = TRUE)
CSA_SHP2 <- geo_join(SHP5, costs8, by = c("group"))
lets_get_started2 <- ggplot(data = CSA_SHP2, aes(fill = CSAstarterRatioPPchange)) +
labs(title = "LCSA percentage point change in new single-family unit permits over total units",
subtitle = " 2010s",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))lets_get_started_2014_2 <- ggplot(data = CSA_SHP2, aes(fill = CSAstarterOverTotPP_2014)) +
labs(title = "LCSA percentage of new single-family unit permits over total units",
subtitle = " 2014",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(low = "darkgreen", mid = "tan", high = "red", midpoint = 2) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))lets_get_started_2019_2 <- ggplot(data = CSA_SHP2, aes(fill = CSAstarterOverTotPP_2019)) +
labs(title = "LCSA percentage of new single-family unit permits over total units",
subtitle = " 2019",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(low = "darkgreen", mid = "tan", high = "red", midpoint = 2) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))lets_get_started_2014_2
lets_get_started_2019_2
lets_get_started2
lets_get_started22 <- ggplot(data = CSA_SHP2, aes(fill = CSAstarterRatioDecade)) +
labs(title = "LCSA percentage of total units from new single-family unit permits",
subtitle = " 2010s",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))lets_get_started22
Domestic migration among counties within a LCSA as well as net migration for LCSAs
flow_2014 <- read_csv("flow_2014.csv")Missing column names filled in: 'X1' [1]
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────
cols(
X1 = col_double(),
GEOID1 = col_character(),
GEOID2 = col_character(),
FULL1_NAME = col_character(),
FULL2_NAME = col_character(),
variable = col_character(),
estimate = col_double(),
moe = col_double()
)flow_2019 <- read_csv("flow_2019.csv")Missing column names filled in: 'X1' [1]
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────
cols(
X1 = col_double(),
GEOID1 = col_character(),
GEOID2 = col_character(),
FULL1_NAME = col_character(),
FULL2_NAME = col_character(),
variable = col_character(),
estimate = col_double(),
moe = col_double()
)flow_2014_2 <- flow_2014 %>%
pivot_wider(., id_cols = c("GEOID1", "GEOID2",
"FULL1_NAME", "FULL2_NAME"
),
names_from = variable,
values_from = c("estimate", "moe")) %>%
select(-"estimate_MOVEDIN", -"estimate_MOVEDOUT", -"moe_MOVEDIN",
-"moe_MOVEDOUT")
flow_2019_2 <- flow_2019 %>%
pivot_wider(., id_cols = c("GEOID1", "GEOID2",
"FULL1_NAME", "FULL2_NAME"
),
names_from = variable,
values_from = c("estimate", "moe")) %>%
select(-"estimate_MOVEDIN", -"estimate_MOVEDOUT", -"moe_MOVEDIN",
-"moe_MOVEDOUT")##Let’s try to get foreign migration as well
flow_2014_for <- flow_2014 %>%
pivot_wider(., id_cols = c("GEOID1", "GEOID2",
"FULL1_NAME", "FULL2_NAME"
),
names_from = variable,
values_from = c("estimate", "moe")) %>%
select(-"estimate_MOVEDNET", -"estimate_MOVEDOUT", -"moe_MOVEDNET",
-"moe_MOVEDOUT")
flow_2014_for <- flow_2014_for %>% filter(is.na(GEOID2))
#----
flow_2014_for <- filter(flow_2014_for, GEOID1 %in% cut2) %>%
select(GEOID = GEOID1, GEOID2, FULL1_NAME, FULL2_NAME, IN_14 = estimate_MOVEDIN, INmoe_14 = moe_MOVEDIN)
flow_2014_for <- full_join(flow_2014_for, costs3, by = c("GEOID")) %>%
select(GEOID, NAME = FULL1_NAME, NAMELSAD, region = FULL2_NAME, IN_14, INmoe_14)
#unique(flow_2014_for$region)
flow_2014_for <- flow_2014_for %>% mutate(group = case_when(TRUE ~ GEOID))
#----
flow_2019_for <- flow_2019 %>%
pivot_wider(., id_cols = c("GEOID1", "GEOID2",
"FULL1_NAME", "FULL2_NAME"
),
names_from = variable,
values_from = c("estimate", "moe")) %>%
select(-"estimate_MOVEDNET", -"estimate_MOVEDOUT", -"moe_MOVEDNET",
-"moe_MOVEDOUT")
flow_2019_for <- flow_2019_for %>% filter(is.na(GEOID2))
#----
flow_2019_for <- filter(flow_2019_for, GEOID1 %in% cut2) %>%
select(GEOID = GEOID1, GEOID2, FULL1_NAME, FULL2_NAME, IN_19 = estimate_MOVEDIN, INmoe_19 = moe_MOVEDIN)
flow_2019_for <- full_join(flow_2019_for, costs3, by = c("GEOID")) %>%
select(GEOID, NAME = FULL1_NAME, NAMELSAD, region = FULL2_NAME, IN_19, INmoe_19)
#unique(flow_2019_for$region)
flow_2019_for <- flow_2019_for %>% mutate(group = case_when(TRUE ~ GEOID))
theBIGfor <- merge(flow_2014_for, flow_2019_for,
by=c("GEOID" = "GEOID", "NAME" = "NAME",
"NAMELSAD" = "NAMELSAD",
"region" = "region",
"group" = "group"),
all = TRUE)
#unique(theBIGfor$region)
#----
theBIGfor2 <- theBIGfor %>% select(-NAME, -region, -group)
index <- is.na(theBIGfor2)
theBIGfor2[index] <- 0
theBIGfor2 <- theBIGfor2 %>% mutate(INcounty14_19 = IN_14 + IN_19)
theBIGfor2 <- theBIGfor2 %>% mutate(group = case_when(TRUE ~ GEOID))
theBIGfor3 <- group_by(theBIGfor2, group) %>%
summarise(INcountyTot14_19 = sum(INcounty14_19), do_union = TRUE) %>%
select(GEOID = group, INcountyTot14_19)`summarise()` ungrouping output (override with `.groups` argument)theBIGfor33 <- group_by(theBIGfor2, group) %>%
summarise(INcountyTot14 = sum(IN_14), do_union = TRUE) %>%
select(GEOID = group, INcountyTot14)`summarise()` ungrouping output (override with `.groups` argument)theBIGfor333 <- group_by(theBIGfor2, group) %>%
summarise(INcountyTot19 = sum(IN_19), do_union = TRUE) %>%
select(GEOID = group, INcountyTot19)`summarise()` ungrouping output (override with `.groups` argument)head(flow_2014_2)flow_2014_US <- flow_2014_2 %>%
remove_missing(name = "estimate_MOVEDNET") Removed 9705 rows containing missing values (estimate_MOVEDNET).#just net migration, which means only domestic
CSAcountiesTo <- filter(flow_2014_US, GEOID1 %in% cut2)head(CSAcountiesTo, 50)CSAcountiesToandFrom2 <- filter(CSAcountiesTo, GEOID2 %in% cut2) %>%
select(GEOID = GEOID1, GEOID2, FULL1_NAME, FULL2_NAME, NET = estimate_MOVEDNET, NETmoe = moe_MOVEDNET)
#only net migration from other LCSA counties
big_test <- full_join(CSAcountiesToandFrom2, costs3, by = c("GEOID")) %>%
select(GEOID1 = GEOID, GEOID = GEOID2, FULL1_NAME, NAMELSAD_FULL1_NAME = NAMELSAD, FULL2_NAME, NET, NETmoe)
big_test2 <- full_join(big_test, costs3, by = c("GEOID")) %>%
select(GEOID1, GEOID2 = GEOID, FULL1_NAME, NAMELSAD_FULL1_NAME, FULL2_NAME, NAMELSAD_FULL2_NAME = NAMELSAD, NET, NETmoe)
big_test3 <- big_test2 %>% filter(NAMELSAD_FULL1_NAME == NAMELSAD_FULL2_NAME)
#only net migration from other counties within the same LCSA
big_test4 <- big_test3 %>% mutate(group = case_when(TRUE ~ GEOID1))head(big_test4, 50)CSAcountiesToandFrom3 <- CSAcountiesTo %>%
select(GEOID = GEOID1, GEOID2, FULL1_NAME, FULL2_NAME, NET = estimate_MOVEDNET, NETmoe = moe_MOVEDNET)
big_test5 <- full_join(CSAcountiesToandFrom3, costs3, by = c("GEOID")) %>%
select(GEOID1 = GEOID, GEOID = GEOID2, FULL1_NAME, NAMELSAD_FULL1_NAME = NAMELSAD, FULL2_NAME, NET, NETmoe)
big_test6 <- full_join(big_test5, costs3, by = c("GEOID")) %>%
select(GEOID1, GEOID2 = GEOID, FULL1_NAME, NAMELSAD_FULL1_NAME, FULL2_NAME, NAMELSAD_FULL2_NAME = NAMELSAD, NET, NETmoe)
big_test7 <- big_test6 %>% filter(NAMELSAD_FULL1_NAME != NAMELSAD_FULL2_NAME | is.na(NAMELSAD_FULL2_NAME))
#domestic migration from counties NOT within the same LCSAhead(big_test7, 50)flow_2019_US <- flow_2019_2 %>% remove_missing(name = "estimate_MOVEDNET")Removed 9880 rows containing missing values (estimate_MOVEDNET).CSAcountiesTo2 <- filter(flow_2019_US, GEOID1 %in% cut2)
CSAcountiesToandFrom4 <- filter(CSAcountiesTo2, GEOID2 %in% cut2) %>%
select(GEOID = GEOID1, GEOID2, FULL1_NAME, FULL2_NAME, NET = estimate_MOVEDNET, NETmoe = moe_MOVEDNET)
big_test9 <- full_join(CSAcountiesToandFrom4, costs3, by = c("GEOID")) %>%
select(GEOID1 = GEOID, GEOID = GEOID2, FULL1_NAME, NAMELSAD_FULL1_NAME = NAMELSAD, FULL2_NAME, NET, NETmoe)
big_test10 <- full_join(big_test9, costs3, by = c("GEOID")) %>%
select(GEOID1, GEOID2 = GEOID, FULL1_NAME, NAMELSAD_FULL1_NAME, FULL2_NAME, NAMELSAD_FULL2_NAME = NAMELSAD, NET, NETmoe)
big_test11 <- big_test10 %>%
filter(NAMELSAD_FULL1_NAME == NAMELSAD_FULL2_NAME)
CSAcountiesToandFrom5 <- CSAcountiesTo2 %>%
select(GEOID = GEOID1, GEOID2, FULL1_NAME, FULL2_NAME, NET = estimate_MOVEDNET, NETmoe = moe_MOVEDNET)
big_test13 <- full_join(CSAcountiesToandFrom5, costs3, by = c("GEOID")) %>%
select(GEOID1 = GEOID, GEOID = GEOID2, FULL1_NAME, NAMELSAD_FULL1_NAME = NAMELSAD, FULL2_NAME, NET, NETmoe)
big_test14 <- full_join(big_test13, costs3, by = c("GEOID")) %>%
select(GEOID1, GEOID2 = GEOID, FULL1_NAME, NAMELSAD_FULL1_NAME, FULL2_NAME, NAMELSAD_FULL2_NAME = NAMELSAD, NET, NETmoe)
big_test15 <- big_test14 %>% filter(NAMELSAD_FULL1_NAME != NAMELSAD_FULL2_NAME | is.na(NAMELSAD_FULL2_NAME))
big_test14_INternal <- big_test4 %>% select(GEOID1, FULL1_NAME, NAMELSAD_FULL1_NAME, GEOID2, FULL2_NAME,
NAMELSAD_FULL2_NAME, inNET_14 = NET, inNETmoe_14 = NETmoe)
big_test14_EXternal <- big_test7 %>% select(GEOID1, FULL1_NAME, NAMELSAD_FULL1_NAME, GEOID2, FULL2_NAME,
NAMELSAD_FULL2_NAME, exNET_14 = NET, exNETmoe_14 = NETmoe)
big_test19_INternal <- big_test11 %>% select(GEOID1, FULL1_NAME, NAMELSAD_FULL1_NAME, GEOID2, FULL2_NAME,
NAMELSAD_FULL2_NAME, inNET_19 = NET, inNETmoe_19 = NETmoe)
big_test19_EXternal <- big_test15 %>% select(GEOID1, FULL1_NAME, NAMELSAD_FULL1_NAME, GEOID2, FULL2_NAME,
NAMELSAD_FULL2_NAME, exNET_19 = NET, exNETmoe_19 = NETmoe)
theBIGinny2 <- merge(big_test14_INternal, big_test19_INternal,
by=c("GEOID1" = "GEOID1", "FULL1_NAME" = "FULL1_NAME",
"NAMELSAD_FULL1_NAME" = "NAMELSAD_FULL1_NAME",
"GEOID2" = "GEOID2", "FULL2_NAME" = "FULL2_NAME",
"NAMELSAD_FULL2_NAME" = "NAMELSAD_FULL2_NAME"),
all = TRUE)
index <- is.na(theBIGinny2)
theBIGinny2[index] <- 0
theBIGinny2 <- theBIGinny2 %>%
mutate(inNETcounty14_19 = inNET_14 + inNET_19)
theBIGinny2 <- theBIGinny2 %>%
mutate(group = case_when(TRUE ~ GEOID1))
theBIGinny3 <- group_by(theBIGinny2, group) %>%
summarise(inNETcountyTot14_19 = sum(inNETcounty14_19), do_union = TRUE) %>%
select(GEOID = group, inNETcountyTot14_19)`summarise()` ungrouping output (override with `.groups` argument)theBIGinny33 <- group_by(theBIGinny2, group) %>%
summarise(inNETcountyTot14 = sum(inNET_14), do_union = TRUE) %>%
select(GEOID = group, inNETcountyTot14)`summarise()` ungrouping output (override with `.groups` argument)theBIGinny333 <- group_by(theBIGinny2, group) %>%
summarise(inNETcountyTot19 = sum(inNET_19), do_union = TRUE) %>%
select(GEOID = group, inNETcountyTot19)`summarise()` ungrouping output (override with `.groups` argument)theBIGouty <- merge(big_test14_EXternal, big_test19_EXternal,
by=c("GEOID1" = "GEOID1", "FULL1_NAME" = "FULL1_NAME",
"NAMELSAD_FULL1_NAME" = "NAMELSAD_FULL1_NAME",
"GEOID2" = "GEOID2", "FULL2_NAME" = "FULL2_NAME",
"NAMELSAD_FULL2_NAME" = "NAMELSAD_FULL2_NAME"),
all = TRUE)
index <- is.na(theBIGouty)
theBIGouty[index] <- 0
theBIGouty <- theBIGouty %>% mutate(exNETcounty14_19 = exNET_14 + exNET_19)
theBIGouty <- theBIGouty %>% mutate(group = case_when(TRUE ~ GEOID1))
theBIGouty2 <- group_by(theBIGouty, group) %>%
summarise(exNETcountyTot14_19 = sum(exNETcounty14_19), do_union = TRUE) %>%
select(GEOID = group, exNETcountyTot14_19)`summarise()` ungrouping output (override with `.groups` argument)theBIGouty22 <- group_by(theBIGouty, group) %>%
summarise(exNETcountyTot14 = sum(exNET_14), do_union = TRUE) %>%
select(GEOID = group, exNETcountyTot14)`summarise()` ungrouping output (override with `.groups` argument)theBIGouty222 <- group_by(theBIGouty, group) %>%
summarise(exNETcountyTot19 = sum(exNET_19), do_union = TRUE) %>%
select(GEOID = group, exNETcountyTot19)`summarise()` ungrouping output (override with `.groups` argument)LCSA_13 <- full_join(costs8, theBIGinny3, by = c("GEOID"))
LCSA_133 <- full_join(LCSA_13, theBIGinny33, by = c("GEOID"))
LCSA_1333 <- full_join(LCSA_133, theBIGinny333, by = c("GEOID"))
LCSA_14 <- full_join(LCSA_1333, theBIGouty2, by = c("GEOID"))
LCSA_144 <- full_join(LCSA_14, theBIGouty22, by = c("GEOID"))
LCSA_1444 <- full_join(LCSA_144, theBIGouty222, by = c("GEOID"))
LCSA_14444 <- full_join(LCSA_1444, theBIGfor3, by = c("GEOID"))
LCSA_144444 <- full_join(LCSA_14444, theBIGfor33, by = c("GEOID"))
LCSA_1444444 <- full_join(LCSA_144444, theBIGfor333, by = c("GEOID"))
LCSA_15 <- group_by(LCSA_1444444, group) %>%
mutate(CSAexternalMigrationNET = sum(exNETcountyTot14_19), do_union = TRUE) %>%
mutate(CSAexMigrationNET_14 = sum(exNETcountyTot14), do_union = TRUE) %>%
mutate(CSAexMigrationNET_19 = sum(exNETcountyTot19), do_union = TRUE)head(LCSA_15, 50)How to conceive of population relative to domestic migration wherein there is a one to one relationship is very subjective, I see people wrestle with this when they offer quantitative figures on the matter. I think of population in this context as an amorphous whole. Something that expands and contracts but is fundimentally = to 1. Also worth noting is that the migration figures are five year estimates. So considering those estimates relative to other estimates from a five year span lends to the whole “amorphous whole” paradigm.
head(LCSA_17, 50)countiesSHP6 <- SHP2 %>% select(GEOID, geometry)
countiesSHP8 <- geo_join(countiesSHP6, LCSA_17, by = c("GEOID"))
colnames(countiesSHP8)
Change3 <- ggplot() +
geom_sf(data = countiesSHP8, aes(fill = pointcontributionFromInternal), color = NA) +
labs(title = "percentage point contribution to county population",
subtitle = " 2010s net domestic migration from counties within LCSA",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
theme_void() +
scale_fill_binned(limits = c(-13,13), high = "purple", low = "yellow",
breaks = c(-10, -6, -2, 0, 2, 6, 10)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))ChangePoint_2014 <- ggplot() +
geom_sf(data = countiesSHP8, aes(fill = pointFromInternal_2014), color = NA) +
labs(title = "point contribution to 2014 county population",
subtitle = " Net domestic migration from counties within LCSA",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
theme_void() +
scale_fill_binned(limits = c(-8,8), high = "purple", low = "yellow",
breaks = c(-6, -4, -2, 0, 2, 6)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))ChangePoint_2019 <- ggplot() +
geom_sf(data = countiesSHP8, aes(fill = pointFromInternal_2019), color = NA) +
labs(title = "point contribution to 2019 county population",
subtitle = " Net domestic migration from counties within LCSA",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
theme_void() +
scale_fill_binned(limits = c(-8,8), high = "purple", low = "yellow",
breaks = c(-6, -4, -2, 0, 2, 6)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))ChangePoint_14_19 <- ggplot() +
geom_sf(data = countiesSHP8, aes(fill = pointChangeInternal), color = NA) +
labs(title = "point change from 2014 to 2019 county population",
subtitle = " Net domestic migration from counties within LCSA",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
theme_void() +
scale_fill_binned(limits = c(-2,2), high = "purple", low = "yellow",
breaks = c(-1, 0, 1)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))ChangePoint_2014
ChangePoint_2019
ChangePoint_14_19
Change3
Change4 <- ggplot() +
geom_sf(data = countiesSHP8, aes(fill = pointcontributionFromExternal), color = NA) +
labs(title = "percentage point contribution to county population",
subtitle = " 2010s external net migration from counties outside of shared LCSA",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-25,25), high = "purple", low = "yellow",
breaks = c(-20, -10, 0, 10, 20)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))ChangePoint_ex2014 <- ggplot() +
geom_sf(data = countiesSHP8, aes(fill = pointFromExternal_2014), color = NA) +
labs(title = "point contribution to 2014 county population",
subtitle = " LCSA external net migration",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-15,15), high = "purple", low = "yellow",
breaks = c(-10, -5, 0, 5, 10, 15)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))ChangePoint_ex2019 <- ggplot() +
geom_sf(data = countiesSHP8, aes(fill = pointFromExternal_2019), color = NA) +
labs(title = "point contribution to 2019 county population",
subtitle = " LCSA external net migration",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-15,15), high = "purple", low = "yellow",
breaks = c(-10, -5, 0, 5, 10, 15)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))ChangePoint_ex_14_19 <- ggplot() +
geom_sf(data = countiesSHP8, aes(fill = pointChangeExternal), color = NA) +
labs(title = "point change from 2014 to 2019 county population",
subtitle = " LCSA external net migration",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-1.5,1.5), high = "purple", low = "yellow",
breaks = c(-1.25, -1, -.75, -.5, -.25, 0, .25)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))ChangePoint_ex2014
ChangePoint_ex2019
ChangePoint_ex_14_19
Change4
LCSA_17 <- LCSA_17 %>%
mutate(CSAstarterRatioDecade = sum(CSAunitsOverTotalUnits_2019*StarterRatioDecade),
do_union = TRUE)
CSAshp3 <- geo_join(SHP5, LCSA_17, by = c("group"))
CSAchange3 <- ggplot(data = CSAshp3, aes(fill = CSApointcontributionFromExternal)) +
labs(title = "percentage point contribution to population",
subtitle = " 2010s net migration",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))CSAchange14 <- ggplot(data = CSAshp3, aes(fill = CSApointcontributionFromExternal_14)) +
labs(title = "point contribution to 2014 LCSA population",
subtitle = " LCSA net migration",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))CSAchange19 <- ggplot(data = CSAshp3, aes(fill = CSApointcontributionFromExternal_19)) +
labs(title = "point contribution to 2019 LCSA population",
subtitle = " LCSA net migration",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))CSApointChange <- ggplot(data = CSAshp3, aes(fill = CSApointChangeExternal)) +
labs(title = "point change in LCSA migration",
subtitle = " LCSA net migration",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))CSAchange14
CSAchange19
CSApointChange
CSAchange3
Foreign migration
forChange4 <- ggplot() +
geom_sf(data = countiesSHP8, aes(fill = pointcontributionFromForeign), color = NA) +
labs(title = "percentage point contribution to county population",
subtitle = " 2010s foreign immigration",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-5,7), high = "purple", low = "yellow",
breaks = c(0, .25, 1, 2, 4, 6)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))ChangePoint_for2014 <- ggplot() +
geom_sf(data = countiesSHP8, aes(fill = pointFromForeign_2014), color = NA) +
labs(title = "point contribution to 2014 county population",
subtitle = " foreign immigration",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-4,4), high = "purple", low = "yellow",
breaks = c(0, .3, .6, 1.2, 1.8, 2.4, 3)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))ChangePoint_for2019 <- ggplot() +
geom_sf(data = countiesSHP8, aes(fill = pointFromForeign_2019), color = NA) +
labs(title = "point contribution to 2019 county population",
subtitle = " foreign immigration",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-4,4), high = "purple", low = "yellow",
breaks = c(0, .3, .6, 1.2, 1.8, 2.4, 3)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))ChangePoint_for_14_19 <- ggplot() +
geom_sf(data = countiesSHP8, aes(fill = pointChangeForeign), color = NA) +
labs(title = "point change from 2014 to 2019 county population",
subtitle = " foreign immigration",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-3,5), high = "purple", low = "yellow",
breaks = c(-2, -.5, .75, 1.5, 4)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))ChangePoint_for2014
ChangePoint_for2019
ChangePoint_for_14_19
forChange4
CSAchange3for <- ggplot(data = CSAshp3, aes(fill = CSApointcontributionFromForeign)) +
labs(title = "percentage point contribution to population",
subtitle = " 2010s foreign immigration",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 1) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))CSAchange14for <- ggplot(data = CSAshp3, aes(fill = CSApointcontributionFromForeign_14)) +
labs(title = "point contribution to 2014 LCSA population",
subtitle = " LCSA foreign immigration",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = .5) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))CSAchange19for <- ggplot(data = CSAshp3, aes(fill = CSApointcontributionFromForeign_19)) +
labs(title = "point contribution to 2019 LCSA population",
subtitle = " LCSA foreign immigration",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = .5) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))CSApointChangefor <- ggplot(data = CSAshp3, aes(fill = CSApointChangeForeign)) +
labs(title = "point change in LCSA migration",
subtitle = " LCSA foreign immigration",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))CSAchange14for
CSAchange19for
CSApointChangefor
CSAchange3for
Breaking down the research so far…
Lancaster_Omaha_example <- countiesSHP8 %>% filter(str_detect(NAMELSAD, "Lincoln-Beatrice, NE CSA") |
str_detect(NAMELSAD, "Omaha-Council Bluffs-Fremont, NE-IA CSA"))
Lancaster_Omaha_LCSA_example3 <- CSAshp3 %>% filter(str_detect(NAMELSAD, "Lincoln-Beatrice, NE CSA") |
str_detect(NAMELSAD, "Omaha-Council Bluffs-Fremont, NE-IA CSA"))
LancasterLCSA_table <- LCSA_17 %>% filter(str_detect(NAMELSAD, "Lincoln-Beatrice, NE CSA") |
str_detect(NAMELSAD, "Omaha-Council Bluffs-Fremont, NE-IA CSA"))exam <- ggplot(data = Lancaster_Omaha_example, aes(fill = PointChangeFirst)) +
labs(title = 'PointChangeFirst',
subtitle = "percentage point change in housing cost over income",
caption = "CostFirst_2019-CostFirst_2014") +
geom_sf(color = "lightyellow") +
geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
theme_void() +
scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.975, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = NA))exam
exam2 <- ggplot(data = Lancaster_Omaha_example, aes(fill = CostFirst_2019)) +
labs(title = 'CostFirst_2019',
subtitle = "sum((household_type(s)/all_occupied_units)*household_type_cost(s))",
caption = "renter, mortgagor and owner weights sum to 1") +
geom_sf(color = "lightyellow") +
geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
theme_void() +
scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 30) +
theme(legend.justification = c(0, 1),
legend.position = c(.975, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = NA))exam2
exam3 <- ggplot(data = Lancaster_Omaha_LCSA_example3, aes(fill = CSApointChangeFirst)) +
labs(title = 'CSApointChangeFirst',
subtitle = "percentage point change in housing cost over income",
caption = "CSAcostFirst_2019-CSAcostFirst_2014") +
geom_sf(color = "lightyellow") +
geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
theme_void() +
scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.975, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = NA))exam3
exam4 <- ggplot(data = Lancaster_Omaha_LCSA_example3, aes(fill = CSAcostFirst_2019)) +
labs(title = 'CSAcostFirst_2019',
subtitle = "sum((counties_type(s)/LCSAs_units)*counties_type_cost(s))",
caption = "counites' LCSA-occupied-units-normalized resident(s) weights sum to 1") +
geom_sf(color = "lightyellow") +
geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
theme_void() +
scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 30) +
theme(legend.justification = c(0, 1),
legend.position = c(.975, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = NA))exam4
exam5 <- ggplot(data = Lancaster_Omaha_example, aes(fill = StarterRatioPPchange)) +
labs(title = 'StarterRatioPPchange',
subtitle = "point change in new single-family unit permits over total 2019 units",
caption = "(sum(singles_15_19)/Total_19)-(sum(singles_10_14)/Total_19)") +
geom_sf(color = "lightyellow") +
geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
theme_void() +
scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.975, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = NA))exam5
exam55 <- ggplot(data = Lancaster_Omaha_example, aes(fill = StarterRatioDecade)) +
labs(title = 'StarterRatioDecade',
subtitle = "percentage of total units from new single-family unit permits",
caption = "100*(NewStarters_2014+NewStarters_2019)/Units_2019)") +
geom_sf(color = "lightyellow") +
geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
theme_void() +
scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.975, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = NA))exam55
exam6 <- ggplot(data = Lancaster_Omaha_LCSA_example3, aes(fill = CSAstarterRatioPPchange)) +
labs(title = 'CSAstarterRatioPPchange',
subtitle = "sum((countyUnits/CSAunits)*StarterRatioPPchange)",
caption = "counites' LCSA-units-normalized point-change weights sum to 1") +
geom_sf(color = "lightyellow") +
geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
theme_void() +
scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.975, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = NA))exam6
exam66 <- ggplot(data = Lancaster_Omaha_LCSA_example3, aes(fill = CSAstarterRatioDecade)) +
labs(title = 'CSAstarterRatioDecade',
subtitle = "sum(CSAunitsOverTotalUnits_2019*StarterRatioDecade), by LCSA",
caption = "LCSA-units-normalized ratio decade weights sum to 1") +
geom_sf(color = "lightyellow") +
geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
theme_void() +
scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.975, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = NA))exam66
exam7 <- ggplot(data = Lancaster_Omaha_example, aes(fill = pointcontributionFromInternal)) +
labs(title = 'pointcontributionFromInternal',
subtitle = "point contribution to 2019 county pop from NET INternal LCSA migration",
caption = "((sum(intraLCSAnetIM14)+sum(intraLCSAnetIM19))/countyPop19)*100") +
geom_sf(color = "lightyellow") +
geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
theme_void() +
scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.975, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = NA))exam7
exam8 <- ggplot(data = Lancaster_Omaha_example, aes(fill = pointcontributionFromExternal)) +
labs(title = 'pointcontributionFromExternal',
subtitle = "point contribution to 2019 county pop from NET EXternal LCSA migration",
caption = "((sum(exterLCSAnetIM14)+sum(exterLCSAnetIM19))/countyPop19)*100") +
geom_sf(color = "lightyellow") +
geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
theme_void() +
scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.975, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = NA))exam8
exam9 <- ggplot(data = Lancaster_Omaha_LCSA_example3, aes(fill = CSApointcontributionFromExternal)) +
labs(title = 'CSApointcontributionFromExternal',
subtitle = "sum((LCSAexterCountyNET19+LCSAexterCountyNET14)), by LCSA",
caption = "dataframe merged arrivals and departures to preserve NET domestic migrant flows") +
geom_sf(color = "lightyellow") +
geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
theme_void() +
scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.975, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = NA))exam9
##Bringing in the geodemographics
demoControl <- read_csv("demoControl.csv")Missing column names filled in: 'X1' [1]
── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
X1 = col_double(),
year = col_double(),
GEOID = col_character(),
NAME = col_character(),
variable = col_character(),
estimate = col_double(),
moe = col_double()
)demoControl2 <- demoControl %>%
pivot_wider(., id_cols = c("year", "GEOID"),
names_from = variable,
values_from = c("estimate", "moe"))
demoControl3 <- demoControl2 %>%
mutate(hisp_prime_male = estimate_B01001I_009 + estimate_B01001I_010 + estimate_B01001I_011 + estimate_B01001I_012) %>%
mutate(prime_male = estimate_B01001_011 + estimate_B01001_012 + estimate_B01001_013 + estimate_B01001_014 + estimate_B01001_015 + estimate_B01001_016) %>%
mutate(hispMale25_54overMale25_54 = (hisp_prime_male/prime_male)*100) %>%
mutate(postUNDERgrad_female_22_29 = estimate_B01001_034 + estimate_B01001_035) %>%
mutate(fertileFEmale_15_49 = estimate_B01001_030 + estimate_B01001_031 + estimate_B01001_032 + estimate_B01001_033 + estimate_B01001_034 + estimate_B01001_035 +
estimate_B01001_036 + estimate_B01001_037 + estimate_B01001_038 + estimate_B01001_039) %>%
mutate(female22_29overFemale15_49 = (postUNDERgrad_female_22_29/fertileFEmale_15_49)*100) %>%
mutate(whiteMALES_45_54 = estimate_B01001A_012) %>%
mutate(MALES_45_54 = estimate_B01001_015 + estimate_B01001_016) %>%
mutate(whiteMALE45_54overMALE45_54 = (whiteMALES_45_54/MALES_45_54)*100) %>%
mutate(black0_17 = estimate_B01001B_003 + estimate_B01001B_004 + estimate_B01001B_005 + estimate_B01001B_006 + estimate_B01001B_018 +
estimate_B01001B_019 + estimate_B01001B_020 + estimate_B01001B_021) %>%
mutate(asian0_17 = estimate_B01001D_003 + estimate_B01001D_004 + estimate_B01001D_005 + estimate_B01001D_006 + estimate_B01001D_018 +
estimate_B01001D_019 + estimate_B01001D_020 + estimate_B01001D_021) %>%
mutate(youth0_17 = estimate_B01001_003 + estimate_B01001_004 + estimate_B01001_005 + estimate_B01001_006 + estimate_B01001_027 +
estimate_B01001_028 + estimate_B01001_029 + estimate_B01001_030) %>%
mutate(black0_17over0_17 = (black0_17/youth0_17)*100) %>%
mutate(asian0_17over0_17 = (asian0_17/youth0_17)*100) %>%
mutate(pop65plusOVERpop = ((estimate_B01001_020 + estimate_B01001_021 + estimate_B01001_022 + estimate_B01001_023 + estimate_B01001_024 +
estimate_B01001_025 + estimate_B01001_044 + estimate_B01001_045 + estimate_B01001_046 + estimate_B01001_047 +
estimate_B01001_048 + estimate_B01001_049)/ estimate_B01001_001)*100) %>%
mutate(OCCUPIEDrooms = estimate_B25014_001) %>%
mutate(roomsWITHoneORless = estimate_B25014_003 + estimate_B25014_004 + estimate_B25014_009 + estimate_B25014_010) %>%
mutate(roomsWITHmoreTHANone = estimate_B25014_005 + estimate_B25014_006 + estimate_B25014_007 +
estimate_B25014_011 + estimate_B25014_012 + estimate_B25014_013)
demoControl4 <- demoControl3 %>%
select(year, GEOID, hispMale25_54overMale25_54, female22_29overFemale15_49, whiteMALE45_54overMALE45_54, black0_17over0_17,
asian0_17over0_17, pop65plusOVERpop, OCCUPIEDrooms, roomsWITHmoreTHANone, prime_male, fertileFEmale_15_49, MALES_45_54,
youth0_17, pop = estimate_B01001_001) %>%
pivot_wider(., id_cols = c("GEOID"),
names_from = year,
values_from = c("hispMale25_54overMale25_54", "female22_29overFemale15_49", "whiteMALE45_54overMALE45_54",
"black0_17over0_17", "asian0_17over0_17", "pop65plusOVERpop",
"OCCUPIEDrooms", "roomsWITHmoreTHANone", "prime_male", "fertileFEmale_15_49",
"MALES_45_54", "pop", "youth0_17")) %>%
mutate(pointCHANGEhispPRIMEmale = hispMale25_54overMale25_54_2019 - hispMale25_54overMale25_54_2014) %>%
mutate(pointCHANGEpostGRADfertPREV = female22_29overFemale15_49_2019 - female22_29overFemale15_49_2014) %>%
mutate(pointCHANGEpeakWHTmaleEARNERSprev = whiteMALE45_54overMALE45_54_2019 - whiteMALE45_54overMALE45_54_2014) %>%
mutate(pointCHANGEblackYOUTHprev = black0_17over0_17_2019 - black0_17over0_17_2014) %>%
mutate(pointCHANGEasianYOUTHprev = asian0_17over0_17_2019 - asian0_17over0_17_2014) %>%
mutate(pointCHANGEretireePREV = pop65plusOVERpop_2019 - pop65plusOVERpop_2014) %>%
mutate(OCCroomsMOREthanONEocc_19 = (roomsWITHmoreTHANone_2019/OCCUPIEDrooms_2019)*100) %>%
mutate(OCCroomsMOREthanONEocc_14 = (roomsWITHmoreTHANone_2014/OCCUPIEDrooms_2014)*100) %>%
mutate(pointCHNGoccRMmoreTHANone = OCCroomsMOREthanONEocc_19 - OCCroomsMOREthanONEocc_14)##Joining dataframes
density <- SHP2 %>%
st_set_geometry(., NULL) %>%
select(-STATEFP, -COUNTYFP, -COUNTYNS, -AFFGEOID, -NAME, -LSAD, -AWATER)
popDEN <- poppy %>% select(GEOID, TotalPop_2014, TotalPop_2019)
density2 <- density %>% full_join(., popDEN, by = c("GEOID"))
density2 <- density2 %>%
mutate(density_2014 = TotalPop_2014/ALAND) %>%
mutate(density_2019 = TotalPop_2019/ALAND) %>%
mutate(densityPointChange = density_2019 - density_2014) %>%
select(-TotalPop_2014, -TotalPop_2019)
LCSA_17 <- LCSA_17 %>% full_join(., density2, by = c("GEOID")) %>%
mutate(csaALAND = sum(ALAND),
do_union = TRUE)
LCSA_17 <- LCSA_17 %>%
mutate(csaDENSITY_2014 = CSApopTotal_2014/csaALAND) %>%
mutate(csaDENSITY_2019 = CSApopTotal_2019/csaALAND)
LCSA_17 <- LCSA_17 %>%
mutate(pointCHANGEcsaDENSITY = csaDENSITY_2019 - csaDENSITY_2014)
LCSA_18 <- LCSA_17 %>% full_join(., demoControl4, by = c("GEOID"))
LCSA_18 <- LCSA_18 %>%
select(GEOID, NAME,
CostFirst_2014, PointChangeFirst, CostFirst_2019,
StarterOverTotPP_2014, StarterRatioPPchange, StarterOverTotPP_2019, StarterRatioDecade,
pointFromInternal_2014, pointChangeInternal,pointFromInternal_2019, pointcontributionFromInternal,
pointFromExternal_2014, pointChangeExternal, pointFromExternal_2019, pointcontributionFromExternal,
pointFromForeign_2014, pointChangeForeign, pointFromForeign_2019, pointcontributionFromForeign,
hispMale25_54overMale25_54_2014, pointCHANGEhispPRIMEmale, hispMale25_54overMale25_54_2019,
female22_29overFemale15_49_2014, pointCHANGEpostGRADfertPREV, female22_29overFemale15_49_2019,
whiteMALE45_54overMALE45_54_2014, pointCHANGEpeakWHTmaleEARNERSprev, whiteMALE45_54overMALE45_54_2019,
black0_17over0_17_2014, pointCHANGEblackYOUTHprev, black0_17over0_17_2019,
asian0_17over0_17_2014, pointCHANGEasianYOUTHprev, asian0_17over0_17_2019,
pop65plusOVERpop_2014, pointCHANGEretireePREV, pop65plusOVERpop_2019,
OCCroomsMOREthanONEocc_14, pointCHNGoccRMmoreTHANone, OCCroomsMOREthanONEocc_19,
OCCUPIEDrooms_2014, OCCUPIEDrooms_2019,
density_2014, densityPointChange, density_2019,
prime_male_2014, prime_male_2019,
fertileFEmale_15_49_2014, fertileFEmale_15_49_2019,
MALES_45_54_2014, MALES_45_54_2019,
pop_2014, pop_2019,
youth0_17_2014, youth0_17_2019,
NAMELSAD, group, do_union,
CSAcostFirst_2014, CSApointChangeFirst, CSAcostFirst_2019,
CSAstarterOverTotPP_2014, CSAstarterRatioPPchange, CSAstarterOverTotPP_2019, CSAstarterRatioDecade,
CSApointcontributionFromExternal_14, CSApointChangeExternal, CSApointcontributionFromExternal_19, CSApointcontributionFromExternal,
CSApointcontributionFromForeign_14, CSApointChangeForeign, CSApointcontributionFromForeign_19, CSApointcontributionFromForeign,
csaDENSITY_2014, pointCHANGEcsaDENSITY, csaDENSITY_2019) %>%
mutate(csaPRIMEmale_14 = sum(prime_male_2014),
do_union = TRUE) %>%
mutate(csaHISPmalePRIMEprev_14 = sum((prime_male_2014/csaPRIMEmale_14)*hispMale25_54overMale25_54_2014),
do_union = TRUE) %>%
mutate(csaPRIMEmale_19 = sum(prime_male_2019),
do_union = TRUE) %>%
mutate(csaHISPmalePRIMEprev_19 = sum((prime_male_2019/csaPRIMEmale_19)*hispMale25_54overMale25_54_2019),
do_union = TRUE) %>%
mutate(csaPOINTchangeHISPprev = csaHISPmalePRIMEprev_19 - csaHISPmalePRIMEprev_14) %>%
mutate(csaFERTfemale_14 = sum(fertileFEmale_15_49_2014),
do_union = TRUE) %>%
mutate(csaFERTfemalePREV_14 = sum((fertileFEmale_15_49_2014/csaFERTfemale_14)*female22_29overFemale15_49_2014),
do_union = TRUE) %>%
mutate(csaFERTfemale_19 = sum(fertileFEmale_15_49_2019),
do_union = TRUE) %>%
mutate(csaFERTfemalePREV_19 = sum((fertileFEmale_15_49_2019/csaFERTfemale_19)*female22_29overFemale15_49_2019),
do_union = TRUE) %>%
mutate(csaPOINTchangeFERTfemale = csaFERTfemalePREV_19 - csaFERTfemalePREV_14) %>%
mutate(csaPEAKwhiteMALEearn_14 = sum(MALES_45_54_2014),
do_union = TRUE) %>%
mutate(csaWHITEmaleEARN_14 = sum((MALES_45_54_2014/csaPEAKwhiteMALEearn_14)*whiteMALE45_54overMALE45_54_2014),
do_union = TRUE) %>%
mutate(csaPEAKwhiteMALEearn_19 = sum(MALES_45_54_2019),
do_union = TRUE) %>%
mutate(csaWHITEmaleEARN_19 = sum((MALES_45_54_2019/csaPEAKwhiteMALEearn_19)*whiteMALE45_54overMALE45_54_2019),
do_union = TRUE) %>%
mutate(csaPOINTchangeWHITEmaleEARN = csaWHITEmaleEARN_19 - csaWHITEmaleEARN_14) %>%
mutate(csaPOP_14 = sum(pop_2014),
do_union = TRUE) %>%
mutate(csa65plusPREV_14 = sum((pop_2014/csaPOP_14)*pop65plusOVERpop_2014),
do_union = TRUE) %>%
mutate(csaPOP_19 = sum(pop_2019),
do_union = TRUE) %>%
mutate(csa65plusPREV_19 = sum((pop_2019/csaPOP_19)*pop65plusOVERpop_2019),
do_union = TRUE) %>%
mutate(csaPOINTchange65plusPREV = csa65plusPREV_19 - csa65plusPREV_14) %>%
mutate(csaYOUTH_14 = sum(youth0_17_2014),
do_union = TRUE) %>%
mutate(csaBLACKyouthPREV_14 = sum((youth0_17_2014/csaYOUTH_14)*black0_17over0_17_2014),
do_union = TRUE) %>%
mutate(csaYOUTH_19 = sum(youth0_17_2019),
do_union = TRUE) %>%
mutate(csaBLACKyouthPREV_19 = sum((youth0_17_2019/csaYOUTH_19)*black0_17over0_17_2019),
do_union = TRUE) %>%
mutate(csaPOINTchangeBLACKyouthPREV = csaBLACKyouthPREV_19 - csaBLACKyouthPREV_14) %>%
mutate(csaASIANyouthPREV_14 = sum((youth0_17_2014/csaYOUTH_14)*asian0_17over0_17_2014),
do_union = TRUE) %>%
mutate(csaASIANyouthPREV_19 = sum((youth0_17_2019/csaYOUTH_19)*asian0_17over0_17_2019),
do_union = TRUE) %>%
mutate(csaPOINTchangeASIANyouthPREV = csaASIANyouthPREV_19 - csaASIANyouthPREV_14) %>%
mutate(csaOCCrooms_14 = sum(OCCUPIEDrooms_2014),
do_union = TRUE) %>%
mutate(csaOCCrooms_19 = sum(OCCUPIEDrooms_2019),
do_union = TRUE) %>%
mutate(csaOCCrmMOREthanONE_14 = sum((OCCUPIEDrooms_2014/csaOCCrooms_14)*OCCroomsMOREthanONEocc_14),
do_union = TRUE) %>%
mutate(csaOCCrmMOREthanONE_19 = sum((OCCUPIEDrooms_2019/csaOCCrooms_19)*OCCroomsMOREthanONEocc_19),
do_union = TRUE) %>%
mutate(csaPOINTchangeOCCrmMOREthanONE = csaOCCrmMOREthanONE_19 - csaOCCrmMOREthanONE_14)
countiesSHP10 <- countiesSHP6 %>% full_join(., LCSA_18, by = c("GEOID"))
LCSA_SHP10 <- geo_join(SHP5, LCSA_18, by = c("group"))##Making GEOJSONs
jsonnyCounites <- countiesSHP10 %>%
select(GEOID, NAME,
PointChangeFirst, CostFirst_2019,
StarterRatioPPchange, StarterOverTotPP_2019, StarterRatioDecade,
pointChangeInternal, pointFromInternal_2019, pointcontributionFromInternal,
pointChangeExternal, pointFromExternal_2019, pointcontributionFromExternal,
pointChangeForeign, pointFromForeign_2019, pointcontributionFromForeign,
pointCHANGEhispPRIMEmale, hispMale25_54overMale25_54_2019,
pointCHANGEpostGRADfertPREV, female22_29overFemale15_49_2019,
pointCHANGEpeakWHTmaleEARNERSprev, whiteMALE45_54overMALE45_54_2019,
pointCHANGEblackYOUTHprev, black0_17over0_17_2019,
pointCHANGEasianYOUTHprev, asian0_17over0_17_2019,
pointCHANGEretireePREV, pop65plusOVERpop_2019,
pointCHNGoccRMmoreTHANone, OCCroomsMOREthanONEocc_19,
density_2014, densityPointChange, density_2019,
geometry) %>%
filter(GEOID %in% cut2) %>%
mutate(PointChangeFirst = PointChangeFirst) %>%
mutate(CostFirst_2019 = CostFirst_2019) %>%
mutate(StarterRatioPPchange = StarterRatioPPchange) %>%
mutate(StarterOverTotPP_2019 = StarterOverTotPP_2019) %>%
mutate(StarterRatioDecade = StarterRatioDecade) %>%
mutate(pointChangeInternal = pointChangeInternal) %>%
mutate(pointFromInternal_2019 = pointFromInternal_2019) %>%
mutate(pointcontributionFromInternal = pointcontributionFromInternal) %>%
mutate(pointChangeExternal = pointChangeExternal) %>%
mutate(pointFromExternal_2019 = pointFromExternal_2019) %>%
mutate(pointcontributionFromExternal = pointcontributionFromExternal) %>%
mutate(pointChangeForeign = pointChangeForeign) %>%
mutate(pointFromForeign_2019 = pointFromForeign_2019) %>%
mutate(pointcontributionFromForeign = pointcontributionFromForeign) %>%
mutate(pointCHANGEhispPRIMEmale = pointCHANGEhispPRIMEmale) %>%
mutate(hispMale25_54overMale25_54_2019 = hispMale25_54overMale25_54_2019) %>%
mutate(pointCHANGEpostGRADfertPREV = pointCHANGEpostGRADfertPREV) %>%
mutate(female22_29overFemale15_49_2019 = female22_29overFemale15_49_2019) %>%
mutate(pointCHANGEpeakWHTmaleEARNERSprev = pointCHANGEpeakWHTmaleEARNERSprev) %>%
mutate(whiteMALE45_54overMALE45_54_2019 = whiteMALE45_54overMALE45_54_2019) %>%
mutate(pointCHANGEblackYOUTHprev = pointCHANGEblackYOUTHprev) %>%
mutate(black0_17over0_17_2019 = black0_17over0_17_2019) %>%
mutate(pointCHANGEasianYOUTHprev = pointCHANGEasianYOUTHprev) %>%
mutate(asian0_17over0_17_2019 = asian0_17over0_17_2019) %>%
mutate(pointCHANGEretireePREV = pointCHANGEretireePREV) %>%
mutate(pop65plusOVERpop_2019 = pop65plusOVERpop_2019) %>%
mutate(pointCHNGoccRMmoreTHANone = pointCHNGoccRMmoreTHANone) %>%
mutate(OCCroomsMOREthanONEocc_19 = OCCroomsMOREthanONEocc_19)
sf::st_write(jsonnyCounites, dsn = "/Users/cwoodson2/Desktop/Phoenix/jsonnyCounites.geojson",
layer = "jsonnyCounites.geojson")Writing layer `jsonnyCounites.geojson' to data source `/Users/cwoodson2/Desktop/Phoenix/jsonnyCounites.geojson' using driver `GeoJSON'
Writing 1234 features with 33 fields and geometry type 3D Multi Polygon.jsonnyCounites2 <- jsonnyCounites %>%
st_set_geometry(., NULL) %>%
select(-GEOID, -NAME)
corry <- cor(jsonnyCounites2)
jsonnyLCSA <- LCSA_SHP10 %>%
select(group,
CSApointChangeFirst, CSAcostFirst_2019,
CSAstarterRatioPPchange, CSAstarterOverTotPP_2019, CSAstarterRatioDecade,
CSApointChangeExternal, CSApointcontributionFromExternal_19, CSApointcontributionFromExternal,
CSApointChangeForeign, CSApointcontributionFromForeign_19, CSApointcontributionFromForeign,
csaPOINTchangeHISPprev, csaHISPmalePRIMEprev_19,
csaPOINTchangeFERTfemale, csaFERTfemalePREV_19,
csaPOINTchangeWHITEmaleEARN, csaWHITEmaleEARN_19,
csaPOINTchange65plusPREV, csa65plusPREV_19,
csaPOINTchangeBLACKyouthPREV, csaBLACKyouthPREV_19,
csaPOINTchangeASIANyouthPREV, csaASIANyouthPREV_19,
csaPOINTchangeOCCrmMOREthanONE, csaOCCrmMOREthanONE_19,
csaDENSITY_2014, pointCHANGEcsaDENSITY, csaDENSITY_2019,) %>%
mutate(CSApointChangeFirst = CSApointChangeFirst) %>%
mutate(CSAcostFirst_2019 = CSAcostFirst_2019) %>%
mutate(CSAstarterRatioPPchange = CSAstarterRatioPPchange) %>%
mutate(CSAstarterOverTotPP_2019 = CSAstarterOverTotPP_2019) %>%
mutate(CSAstarterRatioDecade = CSAstarterRatioDecade) %>%
mutate(CSApointChangeExternal = CSApointChangeExternal) %>%
mutate(CSApointcontributionFromExternal_19 = CSApointcontributionFromExternal_19) %>%
mutate(CSApointcontributionFromExternal = CSApointcontributionFromExternal) %>%
mutate(CSApointChangeForeign = CSApointChangeForeign) %>%
mutate(CSApointcontributionFromForeign_19 = CSApointcontributionFromForeign_19) %>%
mutate(CSApointcontributionFromForeign = CSApointcontributionFromForeign) %>%
mutate(csaPOINTchangeHISPprev = csaPOINTchangeHISPprev) %>%
mutate(csaHISPmalePRIMEprev_19 = csaHISPmalePRIMEprev_19) %>%
mutate(csaPOINTchangeFERTfemale = csaPOINTchangeFERTfemale) %>%
mutate(csaFERTfemalePREV_19 = csaFERTfemalePREV_19) %>%
mutate(csaPOINTchangeWHITEmaleEARN = csaPOINTchangeWHITEmaleEARN) %>%
mutate(csaWHITEmaleEARN_19 = csaWHITEmaleEARN_19) %>%
mutate(csaPOINTchange65plusPREV = csaPOINTchange65plusPREV) %>%
mutate(csa65plusPREV_19 = csa65plusPREV_19) %>%
mutate(csaPOINTchangeBLACKyouthPREV = csaPOINTchangeBLACKyouthPREV) %>%
mutate(csaBLACKyouthPREV_19 = csaBLACKyouthPREV_19) %>%
mutate(csaPOINTchangeASIANyouthPREV = csaPOINTchangeASIANyouthPREV) %>%
mutate(csaASIANyouthPREV_19 = csaASIANyouthPREV_19) %>%
mutate(csaPOINTchangeOCCrmMOREthanONE = csaPOINTchangeOCCrmMOREthanONE) %>%
mutate(csaOCCrmMOREthanONE_19 = csaOCCrmMOREthanONE_19)
sf::st_write(jsonnyLCSA, dsn = "/Users/cwoodson2/Desktop/Phoenix/jsonnyLCSA.geojson",
layer = "jsonnyLCSA.geojson")Writing layer `jsonnyLCSA.geojson' to data source `/Users/cwoodson2/Desktop/Phoenix/jsonnyLCSA.geojson' using driver `GeoJSON'
Writing 164 features with 29 fields and geometry type Unknown (any).jsonnyLCSA2 <- jsonnyLCSA %>%
st_set_geometry(., NULL) %>%
select(-group)
corry2 <- cor(jsonnyLCSA2)##Mapping geodemographics
hispMale_2014 <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = hispMale25_54overMale25_54_2014), color = NA) +
labs(title = "hispMale25_54overMale25_54_2014",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-40,100), high = "purple", low = "yellow",
breaks = c(0, 5, 10, 20, 40, 80)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))hispMale_2019 <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = hispMale25_54overMale25_54_2019), color = NA) +
labs(title = "hispMale25_54overMale25_54_2019",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-40,100), high = "purple", low = "yellow",
breaks = c(0, 5, 10, 20, 40, 80)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))hispMale_change <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = pointCHANGEhispPRIMEmale), color = NA) +
labs(title = "pointCHANGEhispPRIMEmale",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-15,20), high = "purple", low = "yellow",
breaks = c(-5, 0, 5, 10, 15)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))csaHISPmalePRIMEprev_14 <- ggplot(data = LCSA_SHP10, aes(fill = csaHISPmalePRIMEprev_14)) +
labs(title = "csaHISPmalePRIMEprev_14",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 25) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))csaPOINTchangeHISPprev <- ggplot(data = LCSA_SHP10, aes(fill = csaPOINTchangeHISPprev)) +
labs(title = "csaPOINTchangeHISPprev",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))csaHISPmalePRIMEprev_19 <- ggplot(data = LCSA_SHP10, aes(fill = csaHISPmalePRIMEprev_19)) +
labs(title = "csaHISPmalePRIMEprev_19",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 25) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))hispMale_2014
hispMale_change
hispMale_2019
csaHISPmalePRIMEprev_14
csaPOINTchangeHISPprev
csaHISPmalePRIMEprev_19
female22_29_2014 <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = female22_29overFemale15_49_2014), color = NA) +
labs(title = "female22_29overFemale15_49_2014",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(0,30), high = "purple", low = "yellow",
breaks = c(5, 10, 15, 20, 25)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))female22_29_2019 <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = female22_29overFemale15_49_2019), color = NA) +
labs(title = "female22_29overFemale15_49_2019",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(0,30), high = "purple", low = "yellow",
breaks = c(5, 10, 15, 20, 25)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))pointCHANGEpostGRADfert <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = pointCHANGEpostGRADfertPREV), color = NA) +
labs(title = "pointCHANGEpostGRADfertPREV",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-25,25), high = "purple", low = "yellow",
breaks = c(-15, -10, -5, 0, 5, 10, 15)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))csaFERTfemalePREV_14 <- ggplot(data = LCSA_SHP10, aes(fill = csaFERTfemalePREV_14)) +
labs(title = "csaFERTfemalePREV_14",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 25) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))csaPOINTchangeFERTfemale <- ggplot(data = LCSA_SHP10, aes(fill = csaPOINTchangeFERTfemale)) +
labs(title = "csaPOINTchangeFERTfemale",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))csaFERTfemalePREV_19 <- ggplot(data = LCSA_SHP10, aes(fill = csaFERTfemalePREV_19)) +
labs(title = "csaFERTfemalePREV_19",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 25) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))female22_29_2014
pointCHANGEpostGRADfert
female22_29_2019
csaFERTfemalePREV_14
csaPOINTchangeFERTfemale
csaFERTfemalePREV_19
whiteMALE45_54overMALE45_54_2014 <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = whiteMALE45_54overMALE45_54_2014), color = NA) +
labs(title = "whiteMALE45_54overMALE45_54_2014",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-25,100), high = "purple", low = "yellow",
breaks = c(5, 10, 20, 40, 80)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))pointCHANGEpeakWHTmaleEARNERSprev <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = pointCHANGEpeakWHTmaleEARNERSprev), color = NA) +
labs(title = "pointCHANGEpeakWHTmaleEARNERSprev",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-25,25), high = "purple", low = "yellow",
breaks = c(-15, -10, -5, 0, 5, 10, 15)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))whiteMALE45_54overMALE45_54_2019 <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = whiteMALE45_54overMALE45_54_2019), color = NA) +
labs(title = "whiteMALE45_54overMALE45_54_2019",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-25,100), high = "purple", low = "yellow",
breaks = c(5, 10, 20, 40, 80)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))csaWHITEmaleEARN_14 <- ggplot(data = LCSA_SHP10, aes(fill = csaWHITEmaleEARN_14)) +
labs(title = "csaWHITEmaleEARN_14",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 75) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))csaPOINTchangeWHITEmaleEARN <- ggplot(data = LCSA_SHP10, aes(fill = csaPOINTchangeWHITEmaleEARN)) +
labs(title = "csaPOINTchangeWHITEmaleEARN",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))csaWHITEmaleEARN_19 <- ggplot(data = LCSA_SHP10, aes(fill = csaWHITEmaleEARN_19)) +
labs(title = "csaWHITEmaleEARN_19",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 75) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))whiteMALE45_54overMALE45_54_2014
pointCHANGEpeakWHTmaleEARNERSprev
whiteMALE45_54overMALE45_54_2019
csaWHITEmaleEARN_14
csaPOINTchangeWHITEmaleEARN
csaWHITEmaleEARN_19
black0_17over0_17_2014 <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = black0_17over0_17_2014), color = NA) +
labs(title = "black0_17over0_17_2014",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-25,100), high = "purple", low = "yellow",
breaks = c(5, 10, 20, 40, 80)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))pointCHANGEblackYOUTHprev <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = pointCHANGEblackYOUTHprev), color = NA) +
labs(title = "pointCHANGEblackYOUTHprev",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-12,20), high = "purple", low = "yellow",
breaks = c(-10, -5, 0, 5, 10, 15)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))black0_17over0_17_2019 <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = black0_17over0_17_2019), color = NA) +
labs(title = "black0_17over0_17_2019",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-25,100), high = "purple", low = "yellow",
breaks = c(5, 10, 20, 40, 80)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))csaBLACKyouthPREV_14 <- ggplot(data = LCSA_SHP10, aes(fill = csaBLACKyouthPREV_14)) +
labs(title = "csaBLACKyouthPREV_14",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 25) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))csaPOINTchangeBLACKyouthPREV <- ggplot(data = LCSA_SHP10, aes(fill = csaPOINTchangeBLACKyouthPREV)) +
labs(title = "csaPOINTchangeBLACKyouthPREV",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))csaBLACKyouthPREV_19 <- ggplot(data = LCSA_SHP10, aes(fill = csaBLACKyouthPREV_19)) +
labs(title = "csaBLACKyouthPREV_19",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 25) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))black0_17over0_17_2014
pointCHANGEblackYOUTHprev
black0_17over0_17_2019
csaBLACKyouthPREV_14
csaPOINTchangeBLACKyouthPREV
csaBLACKyouthPREV_19
asian0_17over0_17_2014 <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = asian0_17over0_17_2014), color = NA) +
labs(title = "asian0_17over0_17_2014",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-15,40), high = "purple", low = "yellow",
breaks = c(0, 5, 10, 15, 20, 25)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))pointCHANGEasianYOUTHprev <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = pointCHANGEasianYOUTHprev), color = NA) +
labs(title = "pointCHANGEasianYOUTHprev",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-9,11), high = "purple", low = "yellow",
breaks = c(-6, -2, 0, 2, 6)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))asian0_17over0_17_2019 <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = asian0_17over0_17_2019), color = NA) +
labs(title = "asian0_17over0_17_2019",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-15,40), high = "purple", low = "yellow",
breaks = c(0, 5, 10, 15, 20, 25)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))csaPOINTchangeASIANyouthPREV <- ggplot(data = LCSA_SHP10, aes(fill = csaPOINTchangeASIANyouthPREV)) +
labs(title = "csaPOINTchangeASIANyouthPREV",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))csaASIANyouthPREV_14 <- ggplot(data = LCSA_SHP10, aes(fill = csaASIANyouthPREV_14)) +
labs(title = "csaASIANyouthPREV_14",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 5) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))csaASIANyouthPREV_19 <- ggplot(data = LCSA_SHP10, aes(fill = csaASIANyouthPREV_19)) +
labs(title = "csaASIANyouthPREV_19",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 5) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))asian0_17over0_17_2014
pointCHANGEasianYOUTHprev
asian0_17over0_17_2019
csaASIANyouthPREV_14 
csaPOINTchangeASIANyouthPREV
csaASIANyouthPREV_19
pop65plusOVERpop_2014 <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = pop65plusOVERpop_2014), color = NA) +
labs(title = "pop65plusOVERpop_2014",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-10,40), high = "purple", low = "yellow",
breaks = c(0, 5, 10, 15, 20, 25)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))pointCHANGEretireePREV <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = pointCHANGEretireePREV), color = NA) +
labs(title = "pointCHANGEretireePREV",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-10,20), high = "purple", low = "yellow",
breaks = c(-2, 0, 2, 5, 10)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))pop65plusOVERpop_2019 <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = pop65plusOVERpop_2019), color = NA) +
labs(title = "pop65plusOVERpop_2019",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-10,40), high = "purple", low = "yellow",
breaks = c(0, 5, 10, 15, 20, 25)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))csa65plusPREV_14 <- ggplot(data = LCSA_SHP10, aes(fill = csa65plusPREV_14)) +
labs(title = "csa65plusPREV_14",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 15) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))csaPOINTchange65plusPREV <- ggplot(data = LCSA_SHP10, aes(fill = csaPOINTchange65plusPREV)) +
labs(title = "csaPOINTchange65plusPREV",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))csa65plusPREV_19 <- ggplot(data = LCSA_SHP10, aes(fill = csa65plusPREV_19)) +
labs(title = "csa65plusPREV_19",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 15) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))pop65plusOVERpop_2014
pointCHANGEretireePREV
pop65plusOVERpop_2019
csa65plusPREV_14
csaPOINTchange65plusPREV
csa65plusPREV_19
##occupants per room
OCCroomsMOREthanONEocc_14 <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = OCCroomsMOREthanONEocc_14), color = NA) +
labs(title = "OCCroomsMOREthanONEocc_14",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-5,15), high = "purple", low = "yellow",
breaks = c(0, 2, 4, 6, 8, 10, 12)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))pointCHNGoccRMmoreTHANone <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = pointCHNGoccRMmoreTHANone), color = NA) +
labs(title = "pointCHNGoccRMmoreTHANone",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-7,15), high = "purple", low = "yellow",
breaks = c(-6, -4, -2, 0, 2, 4)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))OCCroomsMOREthanONEocc_19 <- ggplot() +
geom_sf(data = countiesSHP10, aes(fill = OCCroomsMOREthanONEocc_19), color = NA) +
labs(title = "OCCroomsMOREthanONEocc_19",
caption = "U.S. Census Bureau") +
geom_sf(data = SHP5, fill = NA, color = "darkgray") +
geom_sf(color = NA) +
theme_void() +
scale_fill_binned(limits = c(-5,15), high = "purple", low = "yellow",
breaks = c(0, 2, 4, 6, 8, 10, 12)) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))csaOCCrmMOREthanONE_14 <- ggplot(data = LCSA_SHP10, aes(fill = csaOCCrmMOREthanONE_14)) +
labs(title = "csaOCCrmMOREthanONE_14",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 5) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))csaPOINTchangeOCCrmMOREthanONE <- ggplot(data = LCSA_SHP10, aes(fill = csaPOINTchangeOCCrmMOREthanONE)) +
labs(title = "csaPOINTchangeOCCrmMOREthanONE",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))csaOCCrmMOREthanONE_19 <- ggplot(data = LCSA_SHP10, aes(fill = csaOCCrmMOREthanONE_19)) +
labs(title = "csaOCCrmMOREthanONE_19",
caption = "U.S. Census Bureau") +
geom_sf(color = "lightyellow") +
theme_void() +
scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 5) +
theme(legend.justification = c(0, 1),
legend.position = c(.85, .39)) +
theme(legend.title=element_blank()) +
theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))OCCroomsMOREthanONEocc_14
pointCHNGoccRMmoreTHANone
OCCroomsMOREthanONEocc_19
csaOCCrmMOREthanONE_14
csaPOINTchangeOCCrmMOREthanONE
csaOCCrmMOREthanONE_19
k-means and regionalize
note for later: change scale on county migration maps
---
title: "Research_diary"
author: "colby_woodson"
date: "1/23/2023"
output: html_notebook
---

```{r}
library(ggplot2)
library(sf)
library(dplyr)
library(tidyverse)
library(tidycensus)
library(tigris)
library(tidyr)
options(tigris_use_cache = TRUE)
```

## What is an "LCSA," why does it matter and how was it made? A combined statistical area is two adjacent metropolitan or micropolitan statistical areas that have an employment interchange measure (EIM) of at least 15. This research coins the phrase 'large combined statistical area' or LCSA, defined as only those CSAs that contain a metropolitan area. The LSCA is used here as a way to narrow the researcher's scale to an area that contains a population that is engaged in dynamic inter-county activity, such as concievably relocating to be closer to work or to buy a home for a new family. It is thought that this is an insightful way to learn more about housing costs over the 2010s. However, as the boundaries of a CSA 'float' every three to five years, customized LCSAs that contain all the counties that were ever part of an LCSA from 2010 through 2019 were made and statistics derived from county estimates were calculated.

```{r}
mapping_example <- map_dfr(c("AL","GA","TN"),
                   ~{counties(.x, cb = TRUE, year = 2010)})

mapping_example_CSA_09to13 <- combined_statistical_areas(cb = FALSE, year = 2011) %>%
  filter(str_detect(NAME, "AL") | str_detect(NAME, "GA") | str_detect(NAME, "TN")) %>% 
  filter(NAMELSAD == "Chattanooga-Cleveland-Athens, TN-GA CSA")

mapping_example_muni_09to13 <- core_based_statistical_areas(year = 2011) %>% 
  filter(str_detect(NAME, "AL") | str_detect(NAME, "GA") | str_detect(NAME, "TN")) %>%
  filter(str_detect(NAMELSAD, "Metro"))

example_09to13 <- ggplot() + 
  geom_sf(data = mapping_example, fill = "lightgray", color = "gray") +
  geom_sf(data = mapping_example_muni_09to13, fill = "green", color = "green") +
  geom_sf(data = mapping_example_CSA_09to13, fill = NA, color = "red") +
  theme_void()
```

## Example from 09 to 13 ref map

```{r}
example_09to13
```

```{r}
mapping_example_CSA_13to18 <- combined_statistical_areas(cb = FALSE, year = 2015) %>%
  filter(str_detect(NAME, "AL") | str_detect(NAME, "GA") | str_detect(NAME, "TN")) %>% 
  filter(NAMELSAD == "Chattanooga-Cleveland-Dalton, TN-GA-AL CSA")

mapping_example_muni_13to18 <- core_based_statistical_areas(year = 2015) %>% 
  filter(str_detect(NAME, "AL") | str_detect(NAME, "GA") | str_detect(NAME, "TN")) %>%
  filter(str_detect(NAMELSAD, "Metro"))

example_13to18 <- ggplot() + 
  geom_sf(data = mapping_example, fill = "lightgray", color = "gray") +
  geom_sf(data = mapping_example_muni_13to18, fill = "green", color = "green") +
  geom_sf(data = mapping_example_CSA_13to18, fill = NA, color = "red") +
  theme_void()
```

## Example from 13 to 18 ref map

```{r}
example_13to18
```

```{r}
mapping_example_CSA_18to20 <- combined_statistical_areas(cb = FALSE, year = 2019) %>%
  filter(str_detect(NAME, "AL") | str_detect(NAME, "GA") | str_detect(NAME, "TN")) %>% 
  filter(NAMELSAD == "Chattanooga-Cleveland-Dalton, TN-GA CSA" |
         NAMELSAD == "Scottsboro-Fort Payne, AL CSA")

mapping_example_muni_18to20 <- core_based_statistical_areas(year = 2019) %>% 
  filter(str_detect(NAME, "AL") | str_detect(NAME, "GA") | str_detect(NAME, "TN")) %>%
  filter(str_detect(NAMELSAD, "Metro"))

example_18to20 <- ggplot() + 
  geom_sf(data = mapping_example, fill = "lightgray", color = "gray") +
  geom_sf(data = mapping_example_muni_18to20, fill = "green", color = "green") +
  geom_sf(data = mapping_example_CSA_18to20, fill = NA, color = "red") +
  theme_void()
```

## Example from 18 to 20 ref map

```{r}
example_18to20
```

```{r}
customized <- read_csv("LCSA_cut.csv")

cut <- customized$GEOID

mapping_example_pre_cut <- mapping_example %>% select(GEOID = GEO_ID, geometry)

mapping_example_cut <- mapping_example_pre_cut %>% separate(GEOID, c("nope", "GEOID"), "US") %>%
  select(-"nope")

mapping_example2 <- filter(mapping_example_cut, GEOID %in% cut)

mapping_example3 <- geo_join(mapping_example2, customized, by = c("GEOID")) %>% 
  filter(., GEOID %in% cut)

mapping_example4 <- mapping_example3 %>% mutate(group = case_when(TRUE ~ NAMELSAD))

mapping_example4 <- group_by(mapping_example4, group) %>% 
    summarize(do_union = TRUE) %>% filter(!is.na(.)) %>% 
    filter(group == "Chattanooga-Cleveland-Dalton, TN-GA-AL CSA")

customized_example2 <- ggplot() + 
  geom_sf(data = mapping_example, fill = "lightgray", color = "gray") +
  geom_sf(data = mapping_example4, fill = "red", color = NA) +
  theme_void()
```

## Custom 2010s LCSA

```{r}
customized_example2
```

```{r}
costs <- customized %>%
  pivot_wider(., id_cols = c("year", "GEOID", "NAME", "NAMELSAD"),
              names_from = variable, values_from = c("estimate", "moe")) %>%
  select(year, GEOID, NAME, NAMELSAD,
         RentCost = estimate_B25071_001,             RentCostMOE = moe_B25071_001,
         MortgagorCost = estimate_B25092_002,        MortgagorCostMOE = moe_B25092_002,
         OwnerCost = estimate_B25092_003,            OwnerCostMOE = moe_B25092_003,
         MedianMortAndOwnCost = estimate_B25092_001, MedianMortAndOwnMOE = moe_B25092_001,
         Renters = estimate_B25003_003,              RentersMOE = moe_B25003_003,
         Mortgagors = estimate_B25081_002,           MortgagorsMOE = moe_B25081_002,
         Owners = estimate_B25081_008,               OwnersMOE = moe_B25081_008,
         MortAndOwn = estimate_B25003_002,           MortAndOwnMOE = moe_B25003_002,
         OccupiedTotal = estimate_B25003_001,        OccupiedTotalMOE = moe_B25003_001,
         MedianHousingCost = estimate_B25105_001,    MedianHousingCostMOE = moe_B25105_001) %>%
  filter(!is.na(NAMELSAD))

costs2 <- costs %>% select(year, GEOID, NAME, NAMELSAD, RentCost, MortgagorCost, OwnerCost, MedianMortAndOwnCost,
                           Renters, Mortgagors, Owners, MortAndOwn, OccupiedTotal, MedianHousingCost)

head <- costs2 %>% filter(is.na(MortgagorCost))
```

## Recoding zeros for mortgagor costs due to the county essentially containing no mortgagors, this recoding only pertains to Kenedy County

```{r}
head(head)
```

```{r}
index <- is.na(costs2)
costs2[index] <- 0
head <- costs2 %>% filter(str_detect(NAME, "Kenedy County, Texas"))
```

```{r}
head(head)
```

## "CostFirst" weights resident cohorts' costs by their prevelance; differientiates between renters, mortgagors and owners, I'm very partial to this method over the "CostSecond" method that uses the variable with the mortgagors and owners lumped together

```{r}
costs3 <- costs2 %>%
  select(year, GEOID, NAME, NAMELSAD, RentCost, MortgagorCost, OwnerCost, MedianMortAndOwnCost,
         Renters, Mortgagors, Owners, MortAndOwn, OccupiedTotal, MedianHousingCost) %>%
  mutate(RentersOverTotal    = Renters         /OccupiedTotal) %>% 
  mutate(MortgagorsOverTotal = Mortgagors      /OccupiedTotal) %>% 
  mutate(OwnerOverTotal      = Owners          /OccupiedTotal) %>% 
  mutate(MortAndOwnOverTotal = MortAndOwn      /OccupiedTotal) %>% 
  mutate(CostFirst           = RentersOverTotal*RentCost + MortgagorsOverTotal*MortgagorCost + OwnerOverTotal*OwnerCost) %>% 
  mutate(CostSecond          = RentersOverTotal*RentCost + MortAndOwnOverTotal*MedianMortAndOwnCost) %>% 
  mutate(FirstTotal          = RentersOverTotal          + MortgagorsOverTotal +               OwnerOverTotal) %>% 
  mutate(SecondTotal         = RentersOverTotal          + MortAndOwnOverTotal) %>% 
  pivot_wider(id_cols = c("GEOID", "NAME", "NAMELSAD"),
              names_from = year,
              values_from = c("RentCost", "MortgagorCost", "OwnerCost",
                              "MedianMortAndOwnCost", "Renters", "Mortgagors",
                              "Owners", "MortAndOwn", "OccupiedTotal",
                              "RentersOverTotal", "MortgagorsOverTotal", "OwnerOverTotal",
                              "MortAndOwnOverTotal", "CostFirst", "CostSecond",
                              "MedianHousingCost", "FirstTotal", "SecondTotal")) %>% 
  mutate(PointChangeFirst    = CostFirst_2019            - CostFirst_2014) %>%
  mutate(PointChangeSecond   = CostSecond_2019           - CostSecond_2014)
```

```{r}
head(costs3)
```

## Bringing the un-recoded MOEs back into the dataframe

```{r}
costs4 <- costs %>%
  select(year, GEOID, NAME, NAMELSAD, RentCostMOE, MortgagorCostMOE, OwnerCostMOE,
         MedianMortAndOwnMOE, RentersMOE, MortgagorsMOE, OwnersMOE, MortAndOwnMOE,
         OccupiedTotalMOE, MedianHousingCostMOE) %>%
  pivot_wider(id_cols = c("GEOID", "NAME", "NAMELSAD"),
              names_from = year,
              values_from = c("RentCostMOE", "MortgagorCostMOE", "OwnerCostMOE",
                              "MedianMortAndOwnMOE", "RentersMOE", "MortgagorsMOE",
                              "OwnersMOE", "MortAndOwnMOE", "OccupiedTotalMOE",
                              "MedianHousingCostMOE")) %>% 
  full_join(., costs3, by = c("GEOID", "NAME", "NAMELSAD"))

cut2 <- costs4$GEOID
```

```{r}
head(costs4)
```

```{r}
SHP <- map_dfr(c("WA", "OR", "NV", "CA", "ID", "AZ", "UT",
                 "CO", "NM", "TX", "OK", "KS", "NE", "SD",
                 "ND", "MN", "IA", "MO", "AR", "LA", "WI",
                 "IL", "KY", "TN", "MS", "FL", "AL", "GA",
                 "SC", "NC", "VA", "WV", "OH", "MI", "NY",
                 "PA", "VT", "NH", "ME", "MA", "RI", "CT",
                 "NJ", "DE", "MD", "DC", "IN"
                 ), ~{
                   counties(.x, cb = TRUE, year = 2014)})

SHP2 <- filter(SHP, GEOID %in% cut2)

SHP3 <- geo_join(SHP2, costs4, by = c("GEOID"))

SHP4 <- SHP3 %>% mutate(group = case_when(TRUE ~ NAMELSAD))

SHP5 <- group_by(SHP4, group) %>% 
    summarize(do_union = TRUE)

CostsMap <- ggplot() +
  geom_sf(data = SHP4, aes(fill = PointChangeFirst), color = NA) +
  labs(title = "percentage point change in costs over income through the 2010s",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  theme_void() +
  scale_fill_binned(limits = c(-6.00,6.00), high = "purple", low = "yellow",
                    breaks = c(-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
CostsMap_2014 <- ggplot() +
  geom_sf(data = SHP4, aes(fill = CostFirst_2014), color = NA) +
  labs(title = "costs over income 2014",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  theme_void() +
  scale_fill_binned(limits = c(0,40), high = "purple", low = "yellow",
                    breaks = c(12, 18, 24, 30, 36)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
CostsMap_2019 <- ggplot() +
  geom_sf(data = SHP4, aes(fill = CostFirst_2019), color = NA) +
  labs(title = "costs over income 2019",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  theme_void() +
  scale_fill_binned(limits = c(0,40), high = "purple", low = "yellow",
                    breaks = c(12, 18, 24, 30, 36)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
CostsMap_2014
```

```{r}
CostsMap_2019
```

```{r}
CostsMap
```

## Aggrigating to the LCSA by weighting resident cohorts' cost contribution to the LCSA by their county cohorts in the numerator and the total of LCSA occupied units in the demoninator

```{r}
costs5 <- costs4 %>% mutate(group = case_when(TRUE ~ NAMELSAD))

costs6 <- group_by(costs5, group) %>%
  mutate(CSAoccupiedTotal_2014       = sum(OccupiedTotal_2014),
         do_union = TRUE) %>%
  mutate(CSAoccupiedTotal_2019       = sum(OccupiedTotal_2019),
         do_union = TRUE) %>% 
  mutate(CSArenters_2014             = sum(Renters_2014),
         do_union = TRUE) %>% 
  mutate(CSArenters_2019             = sum(Renters_2019),
         do_union = TRUE) %>%
  mutate(CSAmortgagors_2014          = sum(Mortgagors_2014),
         do_union = TRUE) %>% 
  mutate(CSAmortgagors_2019          = sum(Mortgagors_2019),
         do_union = TRUE) %>%
  mutate(CSAowners_2014              = sum(Owners_2014),
         do_union = TRUE) %>% 
  mutate(CSAowners_2019              = sum(Owners_2019),
         do_union = TRUE) %>%
  mutate(CSAmortANDown_2014          = sum(MortAndOwn_2014),
         do_union = TRUE) %>% 
  mutate(CSAmortANDown_2019          = sum(MortAndOwn_2019),
         do_union = TRUE) %>% 
  mutate(CSArentersOverTotal_2014    = Renters_2014   /CSAoccupiedTotal_2014) %>% 
  mutate(CSArentersOverTotal_2019    = Renters_2019   /CSAoccupiedTotal_2019) %>%
  mutate(CSAmortgagorsOverTotal_2014 = Mortgagors_2014/CSAoccupiedTotal_2014) %>% 
  mutate(CSAmortgagorsOverTotal_2019 = Mortgagors_2019/CSAoccupiedTotal_2019) %>%
  mutate(CSAownersOverTotal_2014     = Owners_2014    /CSAoccupiedTotal_2014) %>% 
  mutate(CSAownersOverTotal_2019     = Owners_2019    /CSAoccupiedTotal_2019) %>%
  mutate(CSAmortANDownOverTotal_2014 = MortAndOwn_2014/CSAoccupiedTotal_2014) %>% 
  mutate(CSAmortANDownOverTotal_2019 = MortAndOwn_2019/CSAoccupiedTotal_2019) %>%
  mutate(CSAcostFirst_2014           =
           sum(CSArentersOverTotal_2014*RentCost_2014         +
               CSAmortgagorsOverTotal_2014*MortgagorCost_2014 +
               CSAownersOverTotal_2014*OwnerCost_2014),
         do_union = TRUE) %>% 
  mutate(CSAcostFirst_2019           =
           sum(CSArentersOverTotal_2019*RentCost_2019         +
               CSAmortgagorsOverTotal_2019*MortgagorCost_2019 +
               CSAownersOverTotal_2019*OwnerCost_2019),
         do_union = TRUE) %>%
  mutate(CSAcostSecond_2014          =
           sum(CSArentersOverTotal_2014*RentCost_2014         +
               CSAmortANDownOverTotal_2014*MedianMortAndOwnCost_2014),
         do_union = TRUE) %>% 
  mutate(CSAcostSecond_2019          =
           sum(CSArentersOverTotal_2019*RentCost_2019         +
               CSAmortANDownOverTotal_2019*MedianMortAndOwnCost_2019),
         do_union = TRUE) %>% 
  mutate(tot_test                    =
           sum(CSArentersOverTotal_2014                       +
               CSAmortANDownOverTotal_2014),
         do_union = TRUE) %>% 
  mutate(CSApointChangeFirst         = CSAcostFirst_2019  - CSAcostFirst_2014) %>% 
  mutate(CSApointChangeSecond        = CSAcostSecond_2019 - CSAcostSecond_2014)
```

```{r}
head(costs6)
```

```{r}
costs7 <- costs6 %>%
  select(group, CSApointChangeFirst, CSAcostFirst_2019, CSAcostFirst_2014) %>%
  distinct(., group, .keep_all = TRUE)

SHP6 <- geo_join(SHP5, costs7, by = c("group"))

CSA_SHP <- ggplot(data = SHP6, aes(fill = CSApointChangeFirst)) +
  labs(title = "percentage point change in costs over income through the 2010s",
       caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
CSA_SHP_2014 <- ggplot(data = SHP6, aes(fill = CSAcostFirst_2014)) +
  labs(title = "LCSA costs over income 2014",
       caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(low = "darkgreen", mid = "tan", high = "red", midpoint = 25) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
CSA_SHP_2019 <- ggplot(data = SHP6, aes(fill = CSAcostFirst_2019)) +
  labs(title = "LCSA costs over income 2019",
       caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(low = "darkgreen", mid = "tan", high = "red", midpoint = 25) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
CSA_SHP_2014
```

```{r}
CSA_SHP_2019
```

```{r}
CSA_SHP
```

## Searching for any missing values for starters homes

```{r}
starters <- read_csv("permits.csv")

head_start <- starters %>%
  pivot_wider(id_cols = c("GEOID", "COUNTY"),
              names_from = year,
              values_from = c("StarterHomePermits")) %>%
  filter(GEOID %in% cut2) %>% 
  select(GEOID, COUNTY, p10 = "2010", p11 = "2011", p12 = "2012", p13 = "2013",
         p14 = "2014", p15 = "2015", p16 = "2016", p17 = "2017", p18 = "2018",
         p19 = "2019")

head_start$p10 <- as.numeric(as.character(head_start$p10))
head_start$p11 <- as.numeric(as.character(head_start$p11))
head_start$p12 <- as.numeric(as.character(head_start$p12))
head_start$p13 <- as.numeric(as.character(head_start$p13))
head_start$p14 <- as.numeric(as.character(head_start$p14))
head_start$p15 <- as.numeric(as.character(head_start$p15))
head_start$p16 <- as.numeric(as.character(head_start$p16))
head_start$p17 <- as.numeric(as.character(head_start$p17))
head_start$p18 <- as.numeric(as.character(head_start$p18))
head_start$p19 <- as.numeric(as.character(head_start$p19))

head_start2 <- full_join(costs6, head_start, by = "GEOID") %>%
  filter(is.na(p10) | is.na(p11) | is.na(p12) | is.na(p13) |
         is.na(p14) | is.na(p15) | is.na(p16) | is.na(p17) |
         is.na(p18) | is.na(p19)) %>%
  select(GEOID, NAME, NAMELSAD, p10, p11, p12, p13, p14, p15,
         p16, p17, p18, p19)
```

```{r}
head(head_start2, 22)
```

## The proportional average of starterhomes over 2019's total units will be used to generate a multiplier for the 22 missing values, this makes sense because housing development booms and busts in conjuction year over year in accordance with global macroeconomic trends in all areas in the US. That's the reason building permits are included in so many leading economic indexes.

```{r}
cut3 <- head_start2$GEOID

housing_units <- read_csv("units.csv")

housing_units2 <- housing_units %>%
  filter(GEOID %in% cut2)

housing_units3 <- housing_units2 %>%
  pivot_wider(id_cols = c("year","GEOID", "NAME"),
              names_from = variable,
              values_from = c("estimate", "moe")) %>%
  pivot_wider(id_cols = c("GEOID", "NAME"),
              names_from = year,
              values_from = c("estimate_B25002_001", "moe_B25002_001"))
  
housing_units4 <- housing_units3 %>%
  filter(!GEOID %in% cut3) %>%
  select(GEOID, NAME,
         Units_2014 = estimate_B25002_001_2014,
         Units_2019 = estimate_B25002_001_2019,
         UnitsMOE_2014 = moe_B25002_001_2014,
         UnitsMOE_2019 = moe_B25002_001_2019)

housing_units5 <- full_join(housing_units4, head_start, by = "GEOID") %>%
  select(GEOID, NAME, Units_2014, Units_2019,
         p10, p11, p12, p13, p14,
         p15, p16, p17, p18, p19,
         UnitsMOE_2014, UnitsMOE_2019) %>%
  filter(!GEOID %in% cut3)

uni_tot <- sum(housing_units5$Units_2019)
p10_tot <- sum(housing_units5$p10)
p11_tot <- sum(housing_units5$p11)
p12_tot <- sum(housing_units5$p12)
p13_tot <- sum(housing_units5$p13)
p14_tot <- sum(housing_units5$p14)
p15_tot <- sum(housing_units5$p15)
p16_tot <- sum(housing_units5$p16)
p17_tot <- sum(housing_units5$p17)
p18_tot <- sum(housing_units5$p18)
p19_tot <- sum(housing_units5$p19)

weight10 <- p10_tot/uni_tot
weight11 <- p11_tot/uni_tot
weight12 <- p12_tot/uni_tot
weight13 <- p13_tot/uni_tot
weight14 <- p14_tot/uni_tot
weight15 <- p15_tot/uni_tot
weight16 <- p16_tot/uni_tot
weight17 <- p17_tot/uni_tot
weight18 <- p18_tot/uni_tot
weight19 <- p19_tot/uni_tot

housing_units6 <- housing_units3 %>% filter(GEOID %in% cut3) %>%
  select(GEOID, NAME, Units_2014 = estimate_B25002_001_2014,
         Units_2019 = estimate_B25002_001_2019, UnitsMOE_2014 = moe_B25002_001_2014,
         UnitsMOE_2019 = moe_B25002_001_2019)

housing_units7 <- left_join(housing_units6, head_start2, by = c("GEOID", "NAME")) %>%
  select(GEOID, NAME, Units_2014, Units_2019, p10, p11,
         p12, p13, p14, p15, p16, p17, p18, p19, UnitsMOE_2014,
         UnitsMOE_2019)
```

```{r}
head(housing_units7, 22)
```

## Using 2019's total units for all ten years makes sense here because we're building toward the prensent day's total

```{r}
housing_units8 <- housing_units7 %>%
  mutate(p10 = case_when(is.na(p10) ~ Units_2019*weight10,
                   !is.na(p10) ~ p10)) %>% 
  mutate(p10 = round(p10*1, 0)) %>% 
  mutate(p11 = case_when(is.na(p11) ~ Units_2019*weight11,
                   !is.na(p11) ~ p11)) %>% 
  mutate(p11 = round(p11*1, 0)) %>% 
  mutate(p12 = case_when(is.na(p12) ~ Units_2019*weight12,
                   !is.na(p12) ~ p12)) %>% 
  mutate(p12 = round(p12*1, 0)) %>% 
  mutate(p13 = case_when(is.na(p13) ~ Units_2019*weight13,
                   !is.na(p13) ~ p13)) %>% 
  mutate(p13 = round(p13*1, 0)) %>% 
  mutate(p14 = case_when(is.na(p14) ~ Units_2019*weight14,
                   !is.na(p14) ~ p14)) %>% 
  mutate(p14 = round(p14*1, 0)) %>% 
  mutate(p15 = case_when(is.na(p15) ~ Units_2019*weight15,
                   !is.na(p15) ~ p15)) %>% 
  mutate(p15 = round(p15*1, 0)) %>% 
  mutate(p16 = case_when(is.na(p16) ~ Units_2019*weight16,
                   !is.na(p16) ~ p16)) %>% 
  mutate(p16 = round(p16*1, 0)) %>% 
  mutate(p17 = case_when(is.na(p17) ~ Units_2019*weight17,
                   !is.na(p17) ~ p17)) %>% 
  mutate(p17 = round(p17*1, 0)) %>% 
  mutate(p18 = case_when(is.na(p18) ~ Units_2019*weight18,
                   !is.na(p18) ~ p18)) %>% 
  mutate(p18 = round(p18*1, 0)) %>% 
  mutate(p19 = case_when(is.na(p19) ~ Units_2019*weight19,
                   !is.na(p19) ~ p19)) %>% 
  mutate(p19 = round(p19*1, 0))
```

## There's no need to clap, but you can if you want to

```{r}
head(housing_units8, 22)
```

## Let's put those counties back into the main dataframe

```{r}
housing_units9 <- full_join(housing_units8, housing_units5,
                    by = c("GEOID", "NAME", "Units_2014", "Units_2019", "p10",
                           "p11", "p12", "p13", "p14", "p15", "p16", "p17", "p18",
                           "p19", "UnitsMOE_2014", "UnitsMOE_2019")) %>% 
  mutate(NewStarters_2014      = p10+p11+p12+p13+p14) %>% 
  mutate(NewStarters_2019      = p15+p16+p17+p18+p19) %>% 
  mutate(StarterOverTotPP_2014 = 100*(NewStarters_2014/Units_2014)) %>% 
  mutate(StarterOverTotPP_2019 = 100*(NewStarters_2019/Units_2019)) %>% 
  mutate(StarterRatioPPchange  = StarterOverTotPP_2019-StarterOverTotPP_2014) %>% 
  mutate(StarterRatioDecade    = 100*(NewStarters_2014+NewStarters_2019)/Units_2019)

housing_units10 <- housing_units9 %>%
  select("GEOID", "NAME", "Units_2014", "Units_2019","UnitsMOE_2014", "UnitsMOE_2019",
         "NewStarters_2014", "NewStarters_2019", "StarterOverTotPP_2014", "StarterOverTotPP_2019",
         "StarterRatioPPchange", "StarterRatioDecade")

costs8 <- full_join(housing_units10, costs6, by = c("GEOID", "NAME"))
```

```{r}
SHP7 <- SHP2 %>% select(GEOID, NAME, geometry)

SHP8 <- geo_join(SHP7, costs8, by = c("GEOID"))

lets_get_started <- ggplot() +
  geom_sf(data = SHP8, aes(fill = StarterRatioPPchange), color = NA) +
  labs(title = "percentage point change in new single-family unit permits over total units",
       subtitle = " 2010s, LCSA counties",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  theme_void() +
  scale_fill_binned(limits = c(-13,13), high = "purple", low = "yellow",
                    breaks = c(-10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
lets_get_started_2014 <- ggplot() +
  geom_sf(data = SHP8, aes(fill = StarterOverTotPP_2014), color = NA) +
  labs(title = "single-family unit permits over total units",
       subtitle = " 2014, LCSA counties",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  theme_void() +
  scale_fill_binned(limits = c(-10,26), high = "purple", low = "yellow",
                    breaks = c(0, 2, 4, 6, 8, 10, 12, 18)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
lets_get_started_2019 <- ggplot() +
  geom_sf(data = SHP8, aes(fill = StarterOverTotPP_2019), color = NA) +
  labs(title = "single-family unit permits over total units",
       subtitle = " 2019, LCSA counties",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  theme_void() +
  scale_fill_binned(limits = c(-10,26), high = "purple", low = "yellow",
                    breaks = c(0, 2, 4, 6, 8, 10, 12, 18)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
lets_get_started_2014
```

```{r}
lets_get_started_2019
```

```{r}
lets_get_started
```

```{r}
SHP7 <- SHP2 %>% select(GEOID, NAME, geometry)

SHP8 <- geo_join(SHP7, costs8, by = c("GEOID"))

lets_get_started1 <- ggplot() +
  geom_sf(data = SHP8, aes(fill = StarterRatioDecade), color = NA) +
  labs(title = "percentage of total units from new single-family unit permits",
       subtitle = " 2010s, LCSA counties",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  theme_void() +
  scale_fill_binned(limits = c(-13,13), high = "purple", low = "yellow",
                    breaks = c(0, 2, 4, 6, 8, 10)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
lets_get_started1
```

```{r}
costs8 <- group_by(costs8, group) %>%
  mutate(CSAunitsTotal_2019 = sum(Units_2019),
         do_union = TRUE) %>% 
  mutate(CSAunitsTotal_2014 = sum(Units_2014),
         do_union = TRUE) %>% 
  mutate(CSAunitsOverTotalUnits_2014 = Units_2014/CSAunitsTotal_2014) %>% 
  mutate(CSAunitsOverTotalUnits_2019 = Units_2019/CSAunitsTotal_2019) %>%
  mutate(CSAstarterRatioPPchange     = sum(CSAunitsOverTotalUnits_2019*StarterRatioPPchange),
         do_union = TRUE) %>% 
  mutate(CSAstarterRatioDecade       = sum(CSAunitsOverTotalUnits_2019*StarterRatioDecade),
         do_union = TRUE) %>% 
  mutate(CSAstarterOverTotPP_2014    = sum(CSAunitsOverTotalUnits_2014*StarterOverTotPP_2014),
         do_union = TRUE) %>% 
  mutate(CSAstarterOverTotPP_2019 = sum(CSAunitsOverTotalUnits_2019*StarterOverTotPP_2019),
         do_union = TRUE)

CSA_SHP2 <- geo_join(SHP5, costs8, by = c("group"))

lets_get_started2 <- ggplot(data = CSA_SHP2, aes(fill = CSAstarterRatioPPchange)) +
     labs(title = "LCSA percentage point change in new single-family unit permits over total units",
          subtitle = " 2010s",
       caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
    theme(legend.title=element_blank()) +
    theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
lets_get_started_2014_2 <- ggplot(data = CSA_SHP2, aes(fill = CSAstarterOverTotPP_2014)) +
     labs(title = "LCSA percentage of new single-family unit permits over total units",
          subtitle = " 2014",
       caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(low = "darkgreen", mid = "tan", high = "red", midpoint = 2) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
    theme(legend.title=element_blank()) +
    theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
lets_get_started_2019_2 <- ggplot(data = CSA_SHP2, aes(fill = CSAstarterOverTotPP_2019)) +
     labs(title = "LCSA percentage of new single-family unit permits over total units",
          subtitle = " 2019",
       caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(low = "darkgreen", mid = "tan", high = "red", midpoint = 2) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
    theme(legend.title=element_blank()) +
    theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
lets_get_started_2014_2
```

```{r}
lets_get_started_2019_2
```

```{r}
lets_get_started2
```

```{r}
lets_get_started22 <- ggplot(data = CSA_SHP2, aes(fill = CSAstarterRatioDecade)) +
     labs(title = "LCSA percentage of total units from new single-family unit permits",
          subtitle = " 2010s",
       caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
    theme(legend.title=element_blank()) +
    theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
lets_get_started22
```

## Domestic migration among counties within a LCSA as well as net migration for LCSAs

```{r}
flow_2014 <- read_csv("flow_2014.csv")

flow_2019 <- read_csv("flow_2019.csv")

flow_2014_2 <- flow_2014 %>%
  pivot_wider(., id_cols = c("GEOID1", "GEOID2",
                             "FULL1_NAME", "FULL2_NAME"
                             ),
              names_from = variable,
              values_from = c("estimate", "moe")) %>% 
  select(-"estimate_MOVEDIN", -"estimate_MOVEDOUT", -"moe_MOVEDIN",
         -"moe_MOVEDOUT")

flow_2019_2 <- flow_2019 %>%
  pivot_wider(., id_cols = c("GEOID1", "GEOID2",
                             "FULL1_NAME", "FULL2_NAME"
                             ),
              names_from = variable,
              values_from = c("estimate", "moe")) %>% 
  select(-"estimate_MOVEDIN", -"estimate_MOVEDOUT", -"moe_MOVEDIN",
         -"moe_MOVEDOUT")
```

##Let's try to get foreign migration as well

```{r}
flow_2014_for <- flow_2014 %>%
  pivot_wider(., id_cols = c("GEOID1", "GEOID2",
                             "FULL1_NAME", "FULL2_NAME"
                             ),
              names_from = variable,
              values_from = c("estimate", "moe")) %>% 
  select(-"estimate_MOVEDNET", -"estimate_MOVEDOUT", -"moe_MOVEDNET",
         -"moe_MOVEDOUT")

flow_2014_for <- flow_2014_for %>% filter(is.na(GEOID2))
#----
flow_2014_for <- filter(flow_2014_for, GEOID1 %in% cut2) %>%
  select(GEOID = GEOID1, GEOID2, FULL1_NAME, FULL2_NAME, IN_14 = estimate_MOVEDIN, INmoe_14 = moe_MOVEDIN)


flow_2014_for <- full_join(flow_2014_for, costs3, by = c("GEOID")) %>% 
  select(GEOID, NAME = FULL1_NAME, NAMELSAD, region = FULL2_NAME, IN_14, INmoe_14)
#unique(flow_2014_for$region)

flow_2014_for <- flow_2014_for %>% mutate(group = case_when(TRUE ~ GEOID))

#----

flow_2019_for <- flow_2019 %>%
  pivot_wider(., id_cols = c("GEOID1", "GEOID2",
                             "FULL1_NAME", "FULL2_NAME"
                             ),
              names_from = variable,
              values_from = c("estimate", "moe")) %>% 
  select(-"estimate_MOVEDNET", -"estimate_MOVEDOUT", -"moe_MOVEDNET",
         -"moe_MOVEDOUT")

flow_2019_for <- flow_2019_for %>% filter(is.na(GEOID2))
#----
flow_2019_for <- filter(flow_2019_for, GEOID1 %in% cut2) %>%
  select(GEOID = GEOID1, GEOID2, FULL1_NAME, FULL2_NAME, IN_19 = estimate_MOVEDIN, INmoe_19 = moe_MOVEDIN)


flow_2019_for <- full_join(flow_2019_for, costs3, by = c("GEOID")) %>% 
  select(GEOID, NAME = FULL1_NAME, NAMELSAD, region = FULL2_NAME, IN_19, INmoe_19)
#unique(flow_2019_for$region)

flow_2019_for <- flow_2019_for %>% mutate(group = case_when(TRUE ~ GEOID))

theBIGfor <- merge(flow_2014_for, flow_2019_for,
                    by=c("GEOID" = "GEOID", "NAME" = "NAME",
                         "NAMELSAD" = "NAMELSAD",
                         "region" = "region",
                         "group" = "group"),
                    all = TRUE)
#unique(theBIGfor$region)

#----
theBIGfor2 <- theBIGfor %>% select(-NAME, -region, -group)

index <- is.na(theBIGfor2)
theBIGfor2[index] <- 0

theBIGfor2 <- theBIGfor2 %>% mutate(INcounty14_19 = IN_14 + IN_19)

theBIGfor2 <- theBIGfor2 %>% mutate(group = case_when(TRUE ~ GEOID))

theBIGfor3 <- group_by(theBIGfor2, group) %>% 
  summarise(INcountyTot14_19 = sum(INcounty14_19), do_union = TRUE) %>%
  select(GEOID = group, INcountyTot14_19)

theBIGfor33 <- group_by(theBIGfor2, group) %>%
  summarise(INcountyTot14    = sum(IN_14), do_union = TRUE) %>% 
  select(GEOID = group, INcountyTot14)

theBIGfor333 <- group_by(theBIGfor2, group) %>%
  summarise(INcountyTot19    = sum(IN_19), do_union = TRUE) %>% 
  select(GEOID = group, INcountyTot19)
```

```{r}
head(flow_2014_2)
```

```{r}
flow_2014_US <- flow_2014_2 %>%
  remove_missing(name = "estimate_MOVEDNET") 
#just net migration, which means only domestic
CSAcountiesTo <- filter(flow_2014_US, GEOID1 %in% cut2)
```

```{r}
head(CSAcountiesTo, 50)
```

```{r}
CSAcountiesToandFrom2 <- filter(CSAcountiesTo, GEOID2 %in% cut2) %>%
  select(GEOID = GEOID1, GEOID2, FULL1_NAME, FULL2_NAME, NET = estimate_MOVEDNET, NETmoe = moe_MOVEDNET)
#only net migration from other LCSA counties
big_test <- full_join(CSAcountiesToandFrom2, costs3, by = c("GEOID")) %>% 
  select(GEOID1 = GEOID, GEOID = GEOID2, FULL1_NAME, NAMELSAD_FULL1_NAME = NAMELSAD, FULL2_NAME, NET, NETmoe)

big_test2 <- full_join(big_test, costs3, by = c("GEOID")) %>% 
  select(GEOID1, GEOID2 = GEOID, FULL1_NAME, NAMELSAD_FULL1_NAME, FULL2_NAME, NAMELSAD_FULL2_NAME = NAMELSAD, NET, NETmoe)

big_test3 <- big_test2 %>% filter(NAMELSAD_FULL1_NAME == NAMELSAD_FULL2_NAME)
#only net migration from other counties within the same LCSA
big_test4 <- big_test3 %>% mutate(group = case_when(TRUE ~ GEOID1))
```

```{r}
head(big_test4, 50)
```

```{r}
CSAcountiesToandFrom3 <- CSAcountiesTo %>%
  select(GEOID = GEOID1, GEOID2, FULL1_NAME, FULL2_NAME, NET = estimate_MOVEDNET, NETmoe = moe_MOVEDNET)

big_test5 <- full_join(CSAcountiesToandFrom3, costs3, by = c("GEOID")) %>% 
  select(GEOID1 = GEOID, GEOID = GEOID2, FULL1_NAME, NAMELSAD_FULL1_NAME = NAMELSAD, FULL2_NAME, NET, NETmoe)

big_test6 <- full_join(big_test5, costs3, by = c("GEOID")) %>% 
  select(GEOID1, GEOID2 = GEOID, FULL1_NAME, NAMELSAD_FULL1_NAME, FULL2_NAME, NAMELSAD_FULL2_NAME = NAMELSAD, NET, NETmoe)

big_test7 <- big_test6 %>% filter(NAMELSAD_FULL1_NAME != NAMELSAD_FULL2_NAME | is.na(NAMELSAD_FULL2_NAME))
#domestic migration from counties NOT within the same LCSA
```

```{r}
head(big_test7, 50)
```

```{r}
flow_2019_US <- flow_2019_2 %>% remove_missing(name = "estimate_MOVEDNET")

CSAcountiesTo2 <- filter(flow_2019_US, GEOID1 %in% cut2)

CSAcountiesToandFrom4 <- filter(CSAcountiesTo2, GEOID2 %in% cut2) %>%
  select(GEOID = GEOID1, GEOID2, FULL1_NAME, FULL2_NAME, NET = estimate_MOVEDNET, NETmoe = moe_MOVEDNET)

big_test9 <- full_join(CSAcountiesToandFrom4, costs3, by = c("GEOID")) %>% 
  select(GEOID1 = GEOID, GEOID = GEOID2, FULL1_NAME, NAMELSAD_FULL1_NAME = NAMELSAD, FULL2_NAME, NET, NETmoe)

big_test10 <- full_join(big_test9, costs3, by = c("GEOID")) %>% 
  select(GEOID1, GEOID2 = GEOID, FULL1_NAME, NAMELSAD_FULL1_NAME, FULL2_NAME, NAMELSAD_FULL2_NAME = NAMELSAD, NET, NETmoe)

big_test11 <- big_test10 %>%
  filter(NAMELSAD_FULL1_NAME == NAMELSAD_FULL2_NAME)

CSAcountiesToandFrom5 <- CSAcountiesTo2 %>%
  select(GEOID = GEOID1, GEOID2, FULL1_NAME, FULL2_NAME, NET = estimate_MOVEDNET, NETmoe = moe_MOVEDNET)

big_test13 <- full_join(CSAcountiesToandFrom5, costs3, by = c("GEOID")) %>% 
  select(GEOID1 = GEOID, GEOID = GEOID2, FULL1_NAME, NAMELSAD_FULL1_NAME = NAMELSAD, FULL2_NAME, NET, NETmoe)

big_test14 <- full_join(big_test13, costs3, by = c("GEOID")) %>%
  select(GEOID1, GEOID2 = GEOID, FULL1_NAME, NAMELSAD_FULL1_NAME, FULL2_NAME, NAMELSAD_FULL2_NAME = NAMELSAD, NET, NETmoe)

big_test15 <- big_test14 %>% filter(NAMELSAD_FULL1_NAME != NAMELSAD_FULL2_NAME | is.na(NAMELSAD_FULL2_NAME))

big_test14_INternal <- big_test4 %>% select(GEOID1, FULL1_NAME, NAMELSAD_FULL1_NAME, GEOID2, FULL2_NAME,
                                            NAMELSAD_FULL2_NAME, inNET_14 = NET, inNETmoe_14 = NETmoe)

big_test14_EXternal <- big_test7 %>% select(GEOID1, FULL1_NAME, NAMELSAD_FULL1_NAME, GEOID2, FULL2_NAME,
                                            NAMELSAD_FULL2_NAME, exNET_14 = NET, exNETmoe_14 = NETmoe)

big_test19_INternal <- big_test11 %>% select(GEOID1, FULL1_NAME, NAMELSAD_FULL1_NAME, GEOID2, FULL2_NAME,
                                             NAMELSAD_FULL2_NAME, inNET_19 = NET, inNETmoe_19 = NETmoe)

big_test19_EXternal <- big_test15 %>% select(GEOID1, FULL1_NAME, NAMELSAD_FULL1_NAME, GEOID2, FULL2_NAME,
                                             NAMELSAD_FULL2_NAME, exNET_19 = NET, exNETmoe_19 = NETmoe)

theBIGinny2 <- merge(big_test14_INternal, big_test19_INternal,
                     by=c("GEOID1" = "GEOID1", "FULL1_NAME" = "FULL1_NAME",
                          "NAMELSAD_FULL1_NAME" = "NAMELSAD_FULL1_NAME",
                          "GEOID2" = "GEOID2", "FULL2_NAME" = "FULL2_NAME",
                          "NAMELSAD_FULL2_NAME" = "NAMELSAD_FULL2_NAME"),
                     all = TRUE)

index <- is.na(theBIGinny2)

theBIGinny2[index] <- 0

theBIGinny2 <- theBIGinny2 %>%
  mutate(inNETcounty14_19 = inNET_14 + inNET_19)

theBIGinny2 <- theBIGinny2 %>%
  mutate(group = case_when(TRUE ~ GEOID1))

theBIGinny3 <- group_by(theBIGinny2, group) %>%
  summarise(inNETcountyTot14_19 = sum(inNETcounty14_19), do_union = TRUE) %>%
  select(GEOID = group, inNETcountyTot14_19)

theBIGinny33 <- group_by(theBIGinny2, group) %>%
  summarise(inNETcountyTot14    = sum(inNET_14), do_union = TRUE) %>% 
  select(GEOID = group, inNETcountyTot14)

theBIGinny333 <- group_by(theBIGinny2, group) %>%
  summarise(inNETcountyTot19    = sum(inNET_19), do_union = TRUE) %>% 
  select(GEOID = group, inNETcountyTot19)

theBIGouty <- merge(big_test14_EXternal, big_test19_EXternal,
                    by=c("GEOID1" = "GEOID1", "FULL1_NAME" = "FULL1_NAME",
                         "NAMELSAD_FULL1_NAME" = "NAMELSAD_FULL1_NAME",
                         "GEOID2" = "GEOID2", "FULL2_NAME" = "FULL2_NAME",
                         "NAMELSAD_FULL2_NAME" = "NAMELSAD_FULL2_NAME"),
                    all = TRUE)

index <- is.na(theBIGouty)
theBIGouty[index] <- 0

theBIGouty <- theBIGouty %>% mutate(exNETcounty14_19 = exNET_14 + exNET_19)

theBIGouty <- theBIGouty %>% mutate(group = case_when(TRUE ~ GEOID1))

theBIGouty2 <- group_by(theBIGouty, group) %>% 
  summarise(exNETcountyTot14_19 = sum(exNETcounty14_19), do_union = TRUE) %>%
  select(GEOID = group, exNETcountyTot14_19)

theBIGouty22 <- group_by(theBIGouty, group) %>%
  summarise(exNETcountyTot14    = sum(exNET_14), do_union = TRUE) %>% 
  select(GEOID = group, exNETcountyTot14)

theBIGouty222 <- group_by(theBIGouty, group) %>%
  summarise(exNETcountyTot19    = sum(exNET_19), do_union = TRUE) %>% 
  select(GEOID = group, exNETcountyTot19)

LCSA_13 <- full_join(costs8, theBIGinny3, by = c("GEOID"))

LCSA_133 <- full_join(LCSA_13, theBIGinny33, by = c("GEOID"))

LCSA_1333 <- full_join(LCSA_133, theBIGinny333, by = c("GEOID"))

LCSA_14 <- full_join(LCSA_1333, theBIGouty2, by = c("GEOID"))

LCSA_144 <- full_join(LCSA_14, theBIGouty22, by = c("GEOID"))

LCSA_1444 <- full_join(LCSA_144, theBIGouty222, by = c("GEOID"))

LCSA_14444 <- full_join(LCSA_1444, theBIGfor3, by = c("GEOID"))

LCSA_144444 <- full_join(LCSA_14444, theBIGfor33, by = c("GEOID"))

LCSA_1444444 <- full_join(LCSA_144444, theBIGfor333, by = c("GEOID"))

LCSA_15 <- group_by(LCSA_1444444, group) %>%
  mutate(CSAexternalMigrationNET = sum(exNETcountyTot14_19), do_union = TRUE) %>% 
  mutate(CSAexMigrationNET_14 = sum(exNETcountyTot14), do_union = TRUE) %>% 
  mutate(CSAexMigrationNET_19 = sum(exNETcountyTot19), do_union = TRUE)
```

```{r}
head(LCSA_15, 50)
```
## How to conceive of population relative to domestic migration wherein there is a one to one relationship is very subjective, I see people wrestle with this when they offer quantitative figures on the matter. I think of population in this context as an amorphous whole. Something that expands and contracts but is fundimentally = to 1. Also worth noting is that the migration figures are five year estimates. So considering those estimates relative to other estimates from a five year span lends to the whole "amorphous whole" paradigm. 
```{r}
poppy <- read_csv("poppy.csv")

poppy <- poppy %>%
  pivot_wider(., id_cols = c("year", "GEOID"),
              names_from = variable,
              values_from = c("estimate", "moe"))

poppy <- poppy %>% 
  pivot_wider(., id_cols = c("GEOID"),
              names_from = year,
              values_from = c("estimate_B01001_001", "moe_B01001_001"))

poppy <- poppy %>% select(GEOID, TotalPop_2019 = "estimate_B01001_001_2019", TotalPopMOE_2019 = "moe_B01001_001_2019",
                          TotalPop_2014 = "estimate_B01001_001_2014", TotalPopMOE_2014 = "moe_B01001_001_2014")

LCSA_16 <- full_join(LCSA_15, poppy, by = c("GEOID"))

LCSA_17 <- LCSA_16 %>% 
  mutate(CSApopTotal_2019 = sum(TotalPop_2019, do_union = TRUE)) %>% 
  mutate(CSApopTotal_2014 = sum(TotalPop_2014, do_union = TRUE)) %>% 
  mutate(pointcontributionFromInternal    = (inNETcountyTot14_19        /TotalPop_2019)*100) %>% 
  mutate(pointcontributionFromExternal    = (exNETcountyTot14_19        /TotalPop_2019)*100) %>% 
  mutate(CSApointcontributionFromExternal = (CSAexternalMigrationNET    /CSApopTotal_2019)*100,
         do_union = TRUE) %>%
  mutate(CSApointcontributionFromExternal_14 = (CSAexMigrationNET_14    /CSApopTotal_2014)*100,
         do_union = TRUE) %>%
  mutate(CSApointcontributionFromExternal_19 = (CSAexMigrationNET_19    /CSApopTotal_2019)*100,
         do_union = TRUE) %>%
  mutate(CSApointChangeExternal = (CSApointcontributionFromExternal_19 - CSApointcontributionFromExternal_14)) %>% 
  mutate(pointFromInternal_2014           = (inNETcountyTot14           /TotalPop_2014)*100) %>% 
  mutate(pointFromInternal_2019           = (inNETcountyTot19           /TotalPop_2019)*100) %>% 
  mutate(pointChangeInternal              = (pointFromInternal_2019 - pointFromInternal_2014)) %>% 
  mutate(pointFromExternal_2014           = (exNETcountyTot14           /TotalPop_2014)*100) %>% 
  mutate(pointFromExternal_2019           = (exNETcountyTot19           /TotalPop_2019)*100) %>% 
  mutate(pointChangeExternal              = (pointFromExternal_2019 - pointFromExternal_2014)) %>% 
  mutate(pointFromForeign_2014 = (INcountyTot14/TotalPop_2014) *100) %>% 
  mutate(pointFromForeign_2019 = (INcountyTot19/TotalPop_2019) *100) %>%
  mutate(pointChangeForeign = pointFromForeign_2019 - pointFromForeign_2014) %>% 
  mutate(pointcontributionFromForeign = (INcountyTot14_19/TotalPop_2019)*100) %>% 
  mutate(CSAforeign14 = sum(INcountyTot14),
         do_union = TRUE) %>% 
  mutate(CSAforeign19 = sum(INcountyTot19),
         do_union = TRUE) %>% 
  mutate(CSAforeignMigration = sum(INcountyTot14_19),
         do_union = TRUE) %>% 
  mutate(CSApointcontributionFromForeign_14 = (CSAforeign14/CSApopTotal_2014)*100,
         do_union = TRUE) %>%
  mutate(CSApointcontributionFromForeign_19 = (CSAforeign19/CSApopTotal_2019)*100,
         do_union = TRUE) %>%
  mutate(CSApointChangeForeign = (CSApointcontributionFromForeign_19 - CSApointcontributionFromForeign_14)) %>% 
  mutate(CSApointcontributionFromForeign = (CSAforeignMigration/CSApopTotal_2019)*100,
         do_union = TRUE)
  
  
```

```{r}
head(LCSA_17, 50)
```

```{r}
countiesSHP6 <- SHP2 %>% select(GEOID, geometry)

countiesSHP8 <- geo_join(countiesSHP6, LCSA_17, by = c("GEOID"))
colnames(countiesSHP8)
Change3 <- ggplot() +
   geom_sf(data = countiesSHP8, aes(fill = pointcontributionFromInternal), color = NA) +
   labs(title = "percentage point contribution to county population",
        subtitle = " 2010s net domestic migration from counties within LCSA",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  theme_void() +
  scale_fill_binned(limits = c(-13,13), high = "purple", low = "yellow",
                    breaks = c(-10, -6, -2, 0, 2, 6, 10)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
ChangePoint_2014 <- ggplot() +
   geom_sf(data = countiesSHP8, aes(fill = pointFromInternal_2014), color = NA) +
   labs(title = "point contribution to 2014 county population",
        subtitle = " Net domestic migration from counties within LCSA",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  theme_void() +
  scale_fill_binned(limits = c(-8,8), high = "purple", low = "yellow",
                    breaks = c(-6, -4, -2, 0, 2, 6)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
ChangePoint_2019 <- ggplot() +
   geom_sf(data = countiesSHP8, aes(fill = pointFromInternal_2019), color = NA) +
   labs(title = "point contribution to 2019 county population",
        subtitle = " Net domestic migration from counties within LCSA",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  theme_void() +
  scale_fill_binned(limits = c(-8,8), high = "purple", low = "yellow",
                    breaks = c(-6, -4, -2, 0, 2, 6)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
ChangePoint_14_19 <- ggplot() +
   geom_sf(data = countiesSHP8, aes(fill = pointChangeInternal), color = NA) +
   labs(title = "point change from 2014 to 2019 county population",
        subtitle = " Net domestic migration from counties within LCSA",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  theme_void() +
  scale_fill_binned(limits = c(-2,2), high = "purple", low = "yellow",
                    breaks = c(-1, 0, 1)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
ChangePoint_2014
```

```{r}
ChangePoint_2019
```

```{r}
ChangePoint_14_19
```

```{r}
Change3
```

```{r}
Change4 <- ggplot() +
  geom_sf(data = countiesSHP8, aes(fill = pointcontributionFromExternal), color = NA) +
  labs(title = "percentage point contribution to county population",
       subtitle = " 2010s external net migration from counties outside of shared LCSA",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-25,25), high = "purple", low = "yellow",
                    breaks = c(-20, -10, 0, 10, 20)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
ChangePoint_ex2014 <- ggplot() +
  geom_sf(data = countiesSHP8, aes(fill = pointFromExternal_2014), color = NA) +
  labs(title = "point contribution to 2014 county population",
        subtitle = " LCSA external net migration",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-15,15), high = "purple", low = "yellow",
                    breaks = c(-10, -5, 0, 5, 10, 15)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
ChangePoint_ex2019 <- ggplot() +
  geom_sf(data = countiesSHP8, aes(fill = pointFromExternal_2019), color = NA) +
  labs(title = "point contribution to 2019 county population",
        subtitle = " LCSA external net migration",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-15,15), high = "purple", low = "yellow",
                    breaks = c(-10, -5, 0, 5, 10, 15)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
ChangePoint_ex_14_19 <- ggplot() +
  geom_sf(data = countiesSHP8, aes(fill = pointChangeExternal), color = NA) +
  labs(title = "point change from 2014 to 2019 county population",
        subtitle = " LCSA external net migration",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-1.5,1.5), high = "purple", low = "yellow",
                    breaks = c(-1.25, -1, -.75, -.5, -.25, 0, .25)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
ChangePoint_ex2014
```

```{r}
ChangePoint_ex2019
```

```{r}
ChangePoint_ex_14_19
```

```{r}
Change4
```

```{r}
LCSA_17 <- LCSA_17 %>% 
  mutate(CSAstarterRatioDecade       = sum(CSAunitsOverTotalUnits_2019*StarterRatioDecade),
         do_union = TRUE)

CSAshp3 <- geo_join(SHP5, LCSA_17, by = c("group"))

CSAchange3 <- ggplot(data = CSAshp3, aes(fill = CSApointcontributionFromExternal)) +
     labs(title = "percentage point contribution to population",
          subtitle = " 2010s net migration",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
CSAchange14 <- ggplot(data = CSAshp3, aes(fill = CSApointcontributionFromExternal_14)) +
     labs(title = "point contribution to 2014 LCSA population",
          subtitle = " LCSA net migration",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
CSAchange19 <- ggplot(data = CSAshp3, aes(fill = CSApointcontributionFromExternal_19)) +
     labs(title = "point contribution to 2019 LCSA population",
          subtitle = " LCSA net migration",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
CSApointChange <- ggplot(data = CSAshp3, aes(fill = CSApointChangeExternal)) +
     labs(title = "point change in LCSA migration",
          subtitle = " LCSA net migration",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
CSAchange14
```

```{r}
CSAchange19
```

```{r}
CSApointChange
```

```{r}
CSAchange3
```

## Foreign migration

```{r}
forChange4 <- ggplot() +
  geom_sf(data = countiesSHP8, aes(fill = pointcontributionFromForeign), color = NA) +
  labs(title = "percentage point contribution to county population",
       subtitle = " 2010s foreign immigration",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-5,7), high = "purple", low = "yellow",
                    breaks = c(0, .25, 1, 2, 4, 6)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
ChangePoint_for2014 <- ggplot() +
  geom_sf(data = countiesSHP8, aes(fill = pointFromForeign_2014), color = NA) +
  labs(title = "point contribution to 2014 county population",
        subtitle = " foreign immigration",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-4,4), high = "purple", low = "yellow",
                    breaks = c(0, .3, .6, 1.2, 1.8, 2.4, 3)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
ChangePoint_for2019 <- ggplot() +
  geom_sf(data = countiesSHP8, aes(fill = pointFromForeign_2019), color = NA) +
  labs(title = "point contribution to 2019 county population",
        subtitle = " foreign immigration",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-4,4), high = "purple", low = "yellow",
                    breaks = c(0, .3, .6, 1.2, 1.8, 2.4, 3)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
ChangePoint_for_14_19 <- ggplot() +
  geom_sf(data = countiesSHP8, aes(fill = pointChangeForeign), color = NA) +
  labs(title = "point change from 2014 to 2019 county population",
        subtitle = " foreign immigration",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-3,5), high = "purple", low = "yellow",
                    breaks = c(-2, -.5, .75, 1.5, 4)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
ChangePoint_for2014
```

```{r}
ChangePoint_for2019
```

```{r}
ChangePoint_for_14_19
```

```{r}
forChange4
```
   
```{r}
CSAchange3for <- ggplot(data = CSAshp3, aes(fill = CSApointcontributionFromForeign)) +
     labs(title = "percentage point contribution to population",
          subtitle = " 2010s foreign immigration",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 1) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
CSAchange14for <- ggplot(data = CSAshp3, aes(fill = CSApointcontributionFromForeign_14)) +
     labs(title = "point contribution to 2014 LCSA population",
          subtitle = " LCSA foreign immigration",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = .5) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
CSAchange19for <- ggplot(data = CSAshp3, aes(fill = CSApointcontributionFromForeign_19)) +
     labs(title = "point contribution to 2019 LCSA population",
          subtitle = " LCSA foreign immigration",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = .5) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
CSApointChangefor <- ggplot(data = CSAshp3, aes(fill = CSApointChangeForeign)) +
     labs(title = "point change in LCSA migration",
          subtitle = " LCSA foreign immigration",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```
   
```{r}
CSAchange14for
```

```{r}
CSAchange19for
```

```{r}
CSApointChangefor
```

```{r}
CSAchange3for
```

## Breaking down the research so far...

```{r}
Lancaster_Omaha_example <- countiesSHP8 %>% filter(str_detect(NAMELSAD, "Lincoln-Beatrice, NE CSA") |
                                            str_detect(NAMELSAD, "Omaha-Council Bluffs-Fremont, NE-IA CSA"))

Lancaster_Omaha_LCSA_example3 <- CSAshp3 %>% filter(str_detect(NAMELSAD, "Lincoln-Beatrice, NE CSA") |
                                              str_detect(NAMELSAD, "Omaha-Council Bluffs-Fremont, NE-IA CSA"))

LancasterLCSA_table <- LCSA_17 %>% filter(str_detect(NAMELSAD, "Lincoln-Beatrice, NE CSA") |
                                          str_detect(NAMELSAD, "Omaha-Council Bluffs-Fremont, NE-IA CSA"))
```

```{r}
exam <- ggplot(data = Lancaster_Omaha_example, aes(fill = PointChangeFirst)) +
     labs(title = 'PointChangeFirst',
          subtitle = "percentage point change in housing cost over income",
          caption = "CostFirst_2019-CostFirst_2014") +
  geom_sf(color = "lightyellow") +
  geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
  theme_void() +
  scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.975, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = NA))
```

```{r}
exam
```

```{r}
exam2 <- ggplot(data = Lancaster_Omaha_example, aes(fill = CostFirst_2019)) +
     labs(title = 'CostFirst_2019',
          subtitle = "sum((household_type(s)/all_occupied_units)*household_type_cost(s))",
          caption = "renter, mortgagor and owner weights sum to 1") +
  geom_sf(color = "lightyellow") +
  geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
  theme_void() +
  scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 30) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.975, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = NA))
```

```{r}
exam2
```

```{r}
exam3 <- ggplot(data = Lancaster_Omaha_LCSA_example3, aes(fill = CSApointChangeFirst)) +
  labs(title = 'CSApointChangeFirst',
          subtitle = "percentage point change in housing cost over income",
          caption = "CSAcostFirst_2019-CSAcostFirst_2014") +
  geom_sf(color = "lightyellow") +
  geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
  theme_void() +
  scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.975, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = NA))
```

```{r}
exam3
```

```{r}
exam4 <- ggplot(data = Lancaster_Omaha_LCSA_example3, aes(fill = CSAcostFirst_2019)) +
  labs(title = 'CSAcostFirst_2019',
       subtitle = "sum((counties_type(s)/LCSAs_units)*counties_type_cost(s))",
       caption = "counites' LCSA-occupied-units-normalized resident(s) weights sum to 1") +
  geom_sf(color = "lightyellow") +
  geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
  theme_void() +
  scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 30) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.975, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = NA))
```

```{r}
exam4
```

```{r}
exam5 <- ggplot(data = Lancaster_Omaha_example, aes(fill = StarterRatioPPchange)) +
     labs(title = 'StarterRatioPPchange',
          subtitle = "point change in new single-family unit permits over total 2019 units",
          caption = "(sum(singles_15_19)/Total_19)-(sum(singles_10_14)/Total_19)") +
  geom_sf(color = "lightyellow") +
  geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
  theme_void() +
  scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.975, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = NA))
```

```{r}
exam5
```

```{r}
exam55 <- ggplot(data = Lancaster_Omaha_example, aes(fill = StarterRatioDecade)) +
     labs(title = 'StarterRatioDecade',
          subtitle = "percentage of total units from new single-family unit permits",
          caption = "100*(NewStarters_2014+NewStarters_2019)/Units_2019)") +
  geom_sf(color = "lightyellow") +
  geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
  theme_void() +
  scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.975, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = NA))
```

```{r}
exam55
```

```{r}
exam6 <- ggplot(data = Lancaster_Omaha_LCSA_example3, aes(fill = CSAstarterRatioPPchange)) +
  labs(title = 'CSAstarterRatioPPchange',
       subtitle = "sum((countyUnits/CSAunits)*StarterRatioPPchange)",
       caption = "counites' LCSA-units-normalized point-change weights sum to 1") +
  geom_sf(color = "lightyellow") +
  geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
  theme_void() +
  scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.975, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = NA))
```

```{r}
exam6
```

```{r}
exam66 <- ggplot(data = Lancaster_Omaha_LCSA_example3, aes(fill = CSAstarterRatioDecade)) +
  labs(title = 'CSAstarterRatioDecade',
       subtitle = "sum(CSAunitsOverTotalUnits_2019*StarterRatioDecade), by LCSA",
       caption = "LCSA-units-normalized ratio decade weights sum to 1") +
  geom_sf(color = "lightyellow") +
  geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
  theme_void() +
  scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.975, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = NA))
```

```{r}
exam66
```

```{r}
exam7 <- ggplot(data = Lancaster_Omaha_example, aes(fill = pointcontributionFromInternal)) +
     labs(title = 'pointcontributionFromInternal',
          subtitle = "point contribution to 2019 county pop from NET INternal LCSA migration",
          caption = "((sum(intraLCSAnetIM14)+sum(intraLCSAnetIM19))/countyPop19)*100") +
  geom_sf(color = "lightyellow") +
  geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
  theme_void() +
  scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.975, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = NA))
```

```{r}
exam7
```

```{r}
exam8 <- ggplot(data = Lancaster_Omaha_example, aes(fill = pointcontributionFromExternal)) +
     labs(title = 'pointcontributionFromExternal',
          subtitle = "point contribution to 2019 county pop from NET EXternal LCSA migration",
          caption = "((sum(exterLCSAnetIM14)+sum(exterLCSAnetIM19))/countyPop19)*100") +
  geom_sf(color = "lightyellow") +
  geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
  theme_void() +
  scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.975, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = NA))
```

```{r}
exam8
```

```{r}
exam9 <- ggplot(data = Lancaster_Omaha_LCSA_example3, aes(fill = CSApointcontributionFromExternal)) +
  labs(title = 'CSApointcontributionFromExternal',
       subtitle = "sum((LCSAexterCountyNET19+LCSAexterCountyNET14)), by LCSA",
       caption = "dataframe merged arrivals and departures to preserve NET domestic migrant flows") +
  geom_sf(color = "lightyellow") +
  geom_sf(data = Lancaster_Omaha_LCSA_example3, fill = NA, color = "red") +
  theme_void() +
  scale_fill_steps2(n.breaks = 3, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.975, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = NA))
```

```{r}
exam9
```

##Bringing in the geodemographics

```{r}
demoControl <- read_csv("demoControl.csv")

demoControl2 <- demoControl %>%
  pivot_wider(., id_cols = c("year", "GEOID"),
              names_from = variable,
              values_from = c("estimate", "moe"))

demoControl3 <- demoControl2 %>%
  mutate(hisp_prime_male = estimate_B01001I_009 + estimate_B01001I_010 + estimate_B01001I_011 + estimate_B01001I_012) %>% 
  mutate(prime_male = estimate_B01001_011 + estimate_B01001_012 + estimate_B01001_013 + estimate_B01001_014 + estimate_B01001_015 + estimate_B01001_016) %>% 
  mutate(hispMale25_54overMale25_54 = (hisp_prime_male/prime_male)*100) %>% 
  mutate(postUNDERgrad_female_22_29 = estimate_B01001_034 + estimate_B01001_035) %>% 
  mutate(fertileFEmale_15_49 = estimate_B01001_030 + estimate_B01001_031 + estimate_B01001_032 + estimate_B01001_033 + estimate_B01001_034 + estimate_B01001_035 +
         estimate_B01001_036 + estimate_B01001_037 + estimate_B01001_038 + estimate_B01001_039) %>% 
  mutate(female22_29overFemale15_49 = (postUNDERgrad_female_22_29/fertileFEmale_15_49)*100) %>% 
  mutate(whiteMALES_45_54 = estimate_B01001A_012) %>% 
  mutate(MALES_45_54 = estimate_B01001_015 + estimate_B01001_016) %>%
  mutate(whiteMALE45_54overMALE45_54 = (whiteMALES_45_54/MALES_45_54)*100) %>%
  mutate(black0_17 = estimate_B01001B_003 + estimate_B01001B_004 + estimate_B01001B_005 + estimate_B01001B_006 + estimate_B01001B_018 +
         estimate_B01001B_019 + estimate_B01001B_020 + estimate_B01001B_021) %>% 
  mutate(asian0_17 = estimate_B01001D_003 + estimate_B01001D_004 + estimate_B01001D_005 + estimate_B01001D_006 + estimate_B01001D_018 +
         estimate_B01001D_019 + estimate_B01001D_020 + estimate_B01001D_021) %>% 
  mutate(youth0_17 = estimate_B01001_003 + estimate_B01001_004 + estimate_B01001_005 + estimate_B01001_006 + estimate_B01001_027 +
         estimate_B01001_028 + estimate_B01001_029 + estimate_B01001_030) %>% 
  mutate(black0_17over0_17 = (black0_17/youth0_17)*100) %>% 
  mutate(asian0_17over0_17 = (asian0_17/youth0_17)*100) %>% 
  mutate(pop65plusOVERpop = ((estimate_B01001_020 + estimate_B01001_021 + estimate_B01001_022 + estimate_B01001_023 + estimate_B01001_024 +
                              estimate_B01001_025 + estimate_B01001_044 + estimate_B01001_045 + estimate_B01001_046 + estimate_B01001_047 +
                              estimate_B01001_048 + estimate_B01001_049)/ estimate_B01001_001)*100) %>% 
  mutate(OCCUPIEDrooms = estimate_B25014_001) %>% 
  mutate(roomsWITHoneORless = estimate_B25014_003 + estimate_B25014_004 + estimate_B25014_009 + estimate_B25014_010) %>% 
  mutate(roomsWITHmoreTHANone = estimate_B25014_005 + estimate_B25014_006 + estimate_B25014_007 +
         estimate_B25014_011 + estimate_B25014_012 + estimate_B25014_013)
#MOEs are in demoControl2
demoControl4 <- demoControl3 %>% 
  select(year, GEOID, hispMale25_54overMale25_54, female22_29overFemale15_49, whiteMALE45_54overMALE45_54, black0_17over0_17,
         asian0_17over0_17, pop65plusOVERpop, OCCUPIEDrooms, roomsWITHmoreTHANone, prime_male, fertileFEmale_15_49, MALES_45_54,
         youth0_17, pop = estimate_B01001_001) %>% 
    pivot_wider(., id_cols = c("GEOID"),
              names_from = year,
              values_from = c("hispMale25_54overMale25_54", "female22_29overFemale15_49", "whiteMALE45_54overMALE45_54",
                              "black0_17over0_17", "asian0_17over0_17", "pop65plusOVERpop",
                              "OCCUPIEDrooms", "roomsWITHmoreTHANone", "prime_male", "fertileFEmale_15_49",
                              "MALES_45_54", "pop", "youth0_17")) %>% 
  mutate(pointCHANGEhispPRIMEmale = hispMale25_54overMale25_54_2019 - hispMale25_54overMale25_54_2014) %>% 
  mutate(pointCHANGEpostGRADfertPREV = female22_29overFemale15_49_2019 - female22_29overFemale15_49_2014) %>% 
  mutate(pointCHANGEpeakWHTmaleEARNERSprev = whiteMALE45_54overMALE45_54_2019 - whiteMALE45_54overMALE45_54_2014) %>% 
  mutate(pointCHANGEblackYOUTHprev = black0_17over0_17_2019 - black0_17over0_17_2014) %>% 
  mutate(pointCHANGEasianYOUTHprev = asian0_17over0_17_2019 - asian0_17over0_17_2014) %>% 
  mutate(pointCHANGEretireePREV = pop65plusOVERpop_2019 - pop65plusOVERpop_2014) %>% 
  mutate(OCCroomsMOREthanONEocc_19 = (roomsWITHmoreTHANone_2019/OCCUPIEDrooms_2019)*100) %>% 
  mutate(OCCroomsMOREthanONEocc_14 = (roomsWITHmoreTHANone_2014/OCCUPIEDrooms_2014)*100) %>% 
  mutate(pointCHNGoccRMmoreTHANone = OCCroomsMOREthanONEocc_19 - OCCroomsMOREthanONEocc_14)
```

##Joining dataframes

```{r}
density <- SHP2 %>%
  st_set_geometry(., NULL) %>%
  select(-STATEFP, -COUNTYFP, -COUNTYNS, -AFFGEOID, -NAME, -LSAD, -AWATER)

popDEN <- poppy %>% select(GEOID, TotalPop_2014, TotalPop_2019)
  
density2 <- density %>% full_join(., popDEN, by = c("GEOID"))

density2 <- density2 %>% 
  mutate(density_2014 = TotalPop_2014/ALAND) %>% 
  mutate(density_2019 = TotalPop_2019/ALAND) %>% 
  mutate(densityPointChange = density_2019 - density_2014) %>% 
  select(-TotalPop_2014, -TotalPop_2019)

LCSA_17 <- LCSA_17 %>% full_join(., density2, by = c("GEOID")) %>% 
  mutate(csaALAND = sum(ALAND),
         do_union = TRUE)

LCSA_17 <- LCSA_17 %>% 
  mutate(csaDENSITY_2014 = CSApopTotal_2014/csaALAND) %>% 
  mutate(csaDENSITY_2019 = CSApopTotal_2019/csaALAND)

LCSA_17 <- LCSA_17 %>% 
  mutate(pointCHANGEcsaDENSITY = csaDENSITY_2019 - csaDENSITY_2014)

LCSA_18 <- LCSA_17 %>% full_join(., demoControl4, by = c("GEOID"))

LCSA_18 <- LCSA_18 %>%
  select(GEOID, NAME,
         CostFirst_2014, PointChangeFirst, CostFirst_2019,
         StarterOverTotPP_2014, StarterRatioPPchange, StarterOverTotPP_2019, StarterRatioDecade,
         pointFromInternal_2014, pointChangeInternal,pointFromInternal_2019, pointcontributionFromInternal,
         pointFromExternal_2014, pointChangeExternal, pointFromExternal_2019, pointcontributionFromExternal,
         pointFromForeign_2014, pointChangeForeign, pointFromForeign_2019, pointcontributionFromForeign,
         hispMale25_54overMale25_54_2014, pointCHANGEhispPRIMEmale, hispMale25_54overMale25_54_2019,
         female22_29overFemale15_49_2014, pointCHANGEpostGRADfertPREV, female22_29overFemale15_49_2019,
         whiteMALE45_54overMALE45_54_2014, pointCHANGEpeakWHTmaleEARNERSprev, whiteMALE45_54overMALE45_54_2019,
         black0_17over0_17_2014, pointCHANGEblackYOUTHprev, black0_17over0_17_2019,
         asian0_17over0_17_2014, pointCHANGEasianYOUTHprev, asian0_17over0_17_2019,
         pop65plusOVERpop_2014, pointCHANGEretireePREV, pop65plusOVERpop_2019,
         OCCroomsMOREthanONEocc_14, pointCHNGoccRMmoreTHANone, OCCroomsMOREthanONEocc_19,
         OCCUPIEDrooms_2014, OCCUPIEDrooms_2019,
         density_2014, densityPointChange, density_2019,
         prime_male_2014, prime_male_2019,
         fertileFEmale_15_49_2014, fertileFEmale_15_49_2019,
         MALES_45_54_2014, MALES_45_54_2019,
         pop_2014, pop_2019,
         youth0_17_2014, youth0_17_2019,
         NAMELSAD, group, do_union,
         CSAcostFirst_2014, CSApointChangeFirst, CSAcostFirst_2019,
         CSAstarterOverTotPP_2014, CSAstarterRatioPPchange, CSAstarterOverTotPP_2019, CSAstarterRatioDecade,
         CSApointcontributionFromExternal_14, CSApointChangeExternal, CSApointcontributionFromExternal_19, CSApointcontributionFromExternal,
         CSApointcontributionFromForeign_14, CSApointChangeForeign, CSApointcontributionFromForeign_19, CSApointcontributionFromForeign,
         csaDENSITY_2014, pointCHANGEcsaDENSITY, csaDENSITY_2019) %>% 
  mutate(csaPRIMEmale_14 = sum(prime_male_2014),
         do_union = TRUE) %>% 
  mutate(csaHISPmalePRIMEprev_14 = sum((prime_male_2014/csaPRIMEmale_14)*hispMale25_54overMale25_54_2014),
         do_union = TRUE) %>% 
  mutate(csaPRIMEmale_19 = sum(prime_male_2019),
         do_union = TRUE) %>% 
  mutate(csaHISPmalePRIMEprev_19 = sum((prime_male_2019/csaPRIMEmale_19)*hispMale25_54overMale25_54_2019),
         do_union = TRUE) %>% 
  mutate(csaPOINTchangeHISPprev = csaHISPmalePRIMEprev_19 - csaHISPmalePRIMEprev_14) %>% 
  mutate(csaFERTfemale_14 = sum(fertileFEmale_15_49_2014),
         do_union = TRUE) %>% 
  mutate(csaFERTfemalePREV_14 = sum((fertileFEmale_15_49_2014/csaFERTfemale_14)*female22_29overFemale15_49_2014),
         do_union = TRUE) %>% 
  mutate(csaFERTfemale_19 = sum(fertileFEmale_15_49_2019),
         do_union = TRUE) %>% 
  mutate(csaFERTfemalePREV_19 = sum((fertileFEmale_15_49_2019/csaFERTfemale_19)*female22_29overFemale15_49_2019),
         do_union = TRUE) %>% 
  mutate(csaPOINTchangeFERTfemale = csaFERTfemalePREV_19 - csaFERTfemalePREV_14) %>% 
  mutate(csaPEAKwhiteMALEearn_14 = sum(MALES_45_54_2014),
         do_union = TRUE) %>% 
  mutate(csaWHITEmaleEARN_14 = sum((MALES_45_54_2014/csaPEAKwhiteMALEearn_14)*whiteMALE45_54overMALE45_54_2014),
         do_union = TRUE) %>% 
  mutate(csaPEAKwhiteMALEearn_19 = sum(MALES_45_54_2019),
         do_union = TRUE) %>% 
  mutate(csaWHITEmaleEARN_19 = sum((MALES_45_54_2019/csaPEAKwhiteMALEearn_19)*whiteMALE45_54overMALE45_54_2019),
         do_union = TRUE) %>% 
  mutate(csaPOINTchangeWHITEmaleEARN = csaWHITEmaleEARN_19 - csaWHITEmaleEARN_14) %>% 
  mutate(csaPOP_14 = sum(pop_2014),
         do_union = TRUE) %>% 
  mutate(csa65plusPREV_14 = sum((pop_2014/csaPOP_14)*pop65plusOVERpop_2014),
         do_union = TRUE) %>% 
  mutate(csaPOP_19 = sum(pop_2019),
         do_union = TRUE) %>% 
  mutate(csa65plusPREV_19 = sum((pop_2019/csaPOP_19)*pop65plusOVERpop_2019),
         do_union = TRUE) %>% 
  mutate(csaPOINTchange65plusPREV = csa65plusPREV_19 - csa65plusPREV_14) %>% 
  mutate(csaYOUTH_14 = sum(youth0_17_2014),
         do_union = TRUE) %>% 
  mutate(csaBLACKyouthPREV_14 = sum((youth0_17_2014/csaYOUTH_14)*black0_17over0_17_2014),
         do_union = TRUE) %>% 
  mutate(csaYOUTH_19 = sum(youth0_17_2019),
         do_union = TRUE) %>% 
  mutate(csaBLACKyouthPREV_19 = sum((youth0_17_2019/csaYOUTH_19)*black0_17over0_17_2019),
         do_union = TRUE) %>% 
  mutate(csaPOINTchangeBLACKyouthPREV = csaBLACKyouthPREV_19 - csaBLACKyouthPREV_14) %>% 
  mutate(csaASIANyouthPREV_14 = sum((youth0_17_2014/csaYOUTH_14)*asian0_17over0_17_2014),
         do_union = TRUE) %>% 
  mutate(csaASIANyouthPREV_19 = sum((youth0_17_2019/csaYOUTH_19)*asian0_17over0_17_2019),
         do_union = TRUE) %>% 
  mutate(csaPOINTchangeASIANyouthPREV = csaASIANyouthPREV_19 - csaASIANyouthPREV_14) %>% 
  mutate(csaOCCrooms_14 = sum(OCCUPIEDrooms_2014),
         do_union = TRUE) %>% 
  mutate(csaOCCrooms_19 = sum(OCCUPIEDrooms_2019),
         do_union = TRUE) %>% 
  mutate(csaOCCrmMOREthanONE_14 = sum((OCCUPIEDrooms_2014/csaOCCrooms_14)*OCCroomsMOREthanONEocc_14),
         do_union = TRUE) %>% 
  mutate(csaOCCrmMOREthanONE_19 = sum((OCCUPIEDrooms_2019/csaOCCrooms_19)*OCCroomsMOREthanONEocc_19),
         do_union = TRUE) %>% 
  mutate(csaPOINTchangeOCCrmMOREthanONE = csaOCCrmMOREthanONE_19 - csaOCCrmMOREthanONE_14)

countiesSHP10 <- countiesSHP6 %>% full_join(., LCSA_18, by = c("GEOID"))

LCSA_SHP10 <- geo_join(SHP5, LCSA_18, by = c("group"))
```

##Making GEOJSONs

```{r}
jsonnyCounites <- countiesSHP10 %>%
  select(GEOID, NAME,
         PointChangeFirst, CostFirst_2019,
         StarterRatioPPchange, StarterOverTotPP_2019, StarterRatioDecade,
         pointChangeInternal, pointFromInternal_2019, pointcontributionFromInternal,
         pointChangeExternal, pointFromExternal_2019, pointcontributionFromExternal,
         pointChangeForeign, pointFromForeign_2019, pointcontributionFromForeign,
         pointCHANGEhispPRIMEmale, hispMale25_54overMale25_54_2019,
         pointCHANGEpostGRADfertPREV, female22_29overFemale15_49_2019,
         pointCHANGEpeakWHTmaleEARNERSprev, whiteMALE45_54overMALE45_54_2019,
         pointCHANGEblackYOUTHprev, black0_17over0_17_2019,
         pointCHANGEasianYOUTHprev, asian0_17over0_17_2019,
         pointCHANGEretireePREV, pop65plusOVERpop_2019,
         pointCHNGoccRMmoreTHANone, OCCroomsMOREthanONEocc_19,
         density_2014, densityPointChange, density_2019,
         geometry) %>% 
  filter(GEOID %in% cut2) %>%
  mutate(PointChangeFirst = PointChangeFirst) %>% 
  mutate(CostFirst_2019 = CostFirst_2019) %>% 
  mutate(StarterRatioPPchange = StarterRatioPPchange) %>% 
  mutate(StarterOverTotPP_2019 = StarterOverTotPP_2019) %>% 
  mutate(StarterRatioDecade = StarterRatioDecade) %>% 
  mutate(pointChangeInternal = pointChangeInternal) %>% 
  mutate(pointFromInternal_2019 = pointFromInternal_2019) %>% 
  mutate(pointcontributionFromInternal = pointcontributionFromInternal) %>% 
  mutate(pointChangeExternal = pointChangeExternal) %>% 
  mutate(pointFromExternal_2019 = pointFromExternal_2019) %>% 
  mutate(pointcontributionFromExternal = pointcontributionFromExternal) %>%
  mutate(pointChangeForeign = pointChangeForeign) %>%
  mutate(pointFromForeign_2019 = pointFromForeign_2019) %>%
  mutate(pointcontributionFromForeign = pointcontributionFromForeign) %>%
  mutate(pointCHANGEhispPRIMEmale = pointCHANGEhispPRIMEmale) %>% 
  mutate(hispMale25_54overMale25_54_2019 = hispMale25_54overMale25_54_2019) %>% 
  mutate(pointCHANGEpostGRADfertPREV = pointCHANGEpostGRADfertPREV) %>% 
  mutate(female22_29overFemale15_49_2019 = female22_29overFemale15_49_2019) %>% 
  mutate(pointCHANGEpeakWHTmaleEARNERSprev = pointCHANGEpeakWHTmaleEARNERSprev) %>% 
  mutate(whiteMALE45_54overMALE45_54_2019 = whiteMALE45_54overMALE45_54_2019) %>% 
  mutate(pointCHANGEblackYOUTHprev = pointCHANGEblackYOUTHprev) %>% 
  mutate(black0_17over0_17_2019 = black0_17over0_17_2019) %>% 
  mutate(pointCHANGEasianYOUTHprev = pointCHANGEasianYOUTHprev) %>% 
  mutate(asian0_17over0_17_2019 = asian0_17over0_17_2019) %>% 
  mutate(pointCHANGEretireePREV = pointCHANGEretireePREV) %>% 
  mutate(pop65plusOVERpop_2019 = pop65plusOVERpop_2019) %>% 
  mutate(pointCHNGoccRMmoreTHANone = pointCHNGoccRMmoreTHANone) %>% 
  mutate(OCCroomsMOREthanONEocc_19 = OCCroomsMOREthanONEocc_19)

#sf::st_write(jsonnyCounites, dsn = "/Users/cwoodson2/Desktop/Phoenix/jsonnyCounites.geojson",
#             layer = "jsonnyCounites.geojson")

jsonnyCounites2 <- jsonnyCounites %>%
  st_set_geometry(., NULL) %>%
  select(-GEOID, -NAME)

corry <- cor(jsonnyCounites2)

jsonnyLCSA <- LCSA_SHP10 %>% 
  select(group,
         CSApointChangeFirst, CSAcostFirst_2019,
         CSAstarterRatioPPchange, CSAstarterOverTotPP_2019, CSAstarterRatioDecade,
         CSApointChangeExternal, CSApointcontributionFromExternal_19, CSApointcontributionFromExternal,
         CSApointChangeForeign, CSApointcontributionFromForeign_19, CSApointcontributionFromForeign,
         csaPOINTchangeHISPprev, csaHISPmalePRIMEprev_19,
         csaPOINTchangeFERTfemale, csaFERTfemalePREV_19,
         csaPOINTchangeWHITEmaleEARN, csaWHITEmaleEARN_19,
         csaPOINTchange65plusPREV, csa65plusPREV_19,
         csaPOINTchangeBLACKyouthPREV, csaBLACKyouthPREV_19,
         csaPOINTchangeASIANyouthPREV, csaASIANyouthPREV_19,
         csaPOINTchangeOCCrmMOREthanONE, csaOCCrmMOREthanONE_19,
         csaDENSITY_2014, pointCHANGEcsaDENSITY, csaDENSITY_2019,) %>% 
  mutate(CSApointChangeFirst = CSApointChangeFirst) %>% 
  mutate(CSAcostFirst_2019 = CSAcostFirst_2019) %>% 
  mutate(CSAstarterRatioPPchange = CSAstarterRatioPPchange) %>% 
  mutate(CSAstarterOverTotPP_2019 = CSAstarterOverTotPP_2019) %>% 
  mutate(CSAstarterRatioDecade = CSAstarterRatioDecade) %>% 
  mutate(CSApointChangeExternal = CSApointChangeExternal) %>% 
  mutate(CSApointcontributionFromExternal_19 = CSApointcontributionFromExternal_19) %>% 
  mutate(CSApointcontributionFromExternal = CSApointcontributionFromExternal) %>%
  mutate(CSApointChangeForeign = CSApointChangeForeign) %>%
  mutate(CSApointcontributionFromForeign_19 = CSApointcontributionFromForeign_19) %>%
  mutate(CSApointcontributionFromForeign = CSApointcontributionFromForeign) %>%
  mutate(csaPOINTchangeHISPprev = csaPOINTchangeHISPprev) %>% 
  mutate(csaHISPmalePRIMEprev_19 = csaHISPmalePRIMEprev_19) %>% 
  mutate(csaPOINTchangeFERTfemale = csaPOINTchangeFERTfemale) %>% 
  mutate(csaFERTfemalePREV_19 = csaFERTfemalePREV_19) %>% 
  mutate(csaPOINTchangeWHITEmaleEARN = csaPOINTchangeWHITEmaleEARN) %>% 
  mutate(csaWHITEmaleEARN_19 = csaWHITEmaleEARN_19) %>% 
  mutate(csaPOINTchange65plusPREV = csaPOINTchange65plusPREV) %>% 
  mutate(csa65plusPREV_19 = csa65plusPREV_19) %>% 
  mutate(csaPOINTchangeBLACKyouthPREV = csaPOINTchangeBLACKyouthPREV) %>% 
  mutate(csaBLACKyouthPREV_19 = csaBLACKyouthPREV_19) %>% 
  mutate(csaPOINTchangeASIANyouthPREV = csaPOINTchangeASIANyouthPREV) %>% 
  mutate(csaASIANyouthPREV_19 = csaASIANyouthPREV_19) %>% 
  mutate(csaPOINTchangeOCCrmMOREthanONE = csaPOINTchangeOCCrmMOREthanONE) %>% 
  mutate(csaOCCrmMOREthanONE_19 = csaOCCrmMOREthanONE_19)

#sf::st_write(jsonnyLCSA, dsn = "/Users/cwoodson2/Desktop/Phoenix/jsonnyLCSA.geojson",
#             layer = "jsonnyLCSA.geojson")

jsonnyLCSA2 <- jsonnyLCSA %>%
  st_set_geometry(., NULL) %>%
  select(-group)

corry2 <- cor(jsonnyLCSA2)
```

##Mapping geodemographics

```{r}
hispMale_2014 <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = hispMale25_54overMale25_54_2014), color = NA) +
  labs(title = "hispMale25_54overMale25_54_2014",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-40,100), high = "purple", low = "yellow",
                    breaks = c(0, 5, 10, 20, 40, 80)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
hispMale_2019 <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = hispMale25_54overMale25_54_2019), color = NA) +
  labs(title = "hispMale25_54overMale25_54_2019",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-40,100), high = "purple", low = "yellow",
                    breaks = c(0, 5, 10, 20, 40, 80)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
hispMale_change <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = pointCHANGEhispPRIMEmale), color = NA) +
  labs(title = "pointCHANGEhispPRIMEmale",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-15,20), high = "purple", low = "yellow",
                    breaks = c(-5, 0, 5, 10, 15)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
csaHISPmalePRIMEprev_14 <- ggplot(data = LCSA_SHP10, aes(fill = csaHISPmalePRIMEprev_14)) +
     labs(title = "csaHISPmalePRIMEprev_14",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 25) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
csaPOINTchangeHISPprev <- ggplot(data = LCSA_SHP10, aes(fill = csaPOINTchangeHISPprev)) +
     labs(title = "csaPOINTchangeHISPprev",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
csaHISPmalePRIMEprev_19 <- ggplot(data = LCSA_SHP10, aes(fill = csaHISPmalePRIMEprev_19)) +
     labs(title = "csaHISPmalePRIMEprev_19",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 25) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
hispMale_2014
```

```{r}
hispMale_change
```

```{r}
hispMale_2019
```

```{r}
csaHISPmalePRIMEprev_14
```

```{r}
csaPOINTchangeHISPprev
```

```{r}
csaHISPmalePRIMEprev_19
```

```{r}
female22_29_2014 <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = female22_29overFemale15_49_2014), color = NA) +
  labs(title = "female22_29overFemale15_49_2014",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(0,30), high = "purple", low = "yellow",
                    breaks = c(5, 10, 15, 20, 25)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
female22_29_2019 <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = female22_29overFemale15_49_2019), color = NA) +
  labs(title = "female22_29overFemale15_49_2019",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(0,30), high = "purple", low = "yellow",
                    breaks = c(5, 10, 15, 20, 25)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
pointCHANGEpostGRADfert <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = pointCHANGEpostGRADfertPREV), color = NA) +
  labs(title = "pointCHANGEpostGRADfertPREV",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-25,25), high = "purple", low = "yellow",
                    breaks = c(-15, -10, -5, 0, 5, 10, 15)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
csaFERTfemalePREV_14 <- ggplot(data = LCSA_SHP10, aes(fill = csaFERTfemalePREV_14)) +
     labs(title = "csaFERTfemalePREV_14",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 25) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
csaPOINTchangeFERTfemale <- ggplot(data = LCSA_SHP10, aes(fill = csaPOINTchangeFERTfemale)) +
     labs(title = "csaPOINTchangeFERTfemale",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
csaFERTfemalePREV_19 <- ggplot(data = LCSA_SHP10, aes(fill = csaFERTfemalePREV_19)) +
     labs(title = "csaFERTfemalePREV_19",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 25) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
female22_29_2014
```

```{r}
pointCHANGEpostGRADfert
```

```{r}
female22_29_2019
```

```{r}
csaFERTfemalePREV_14
```

```{r}
csaPOINTchangeFERTfemale
```

```{r}
csaFERTfemalePREV_19
```

```{r}
whiteMALE45_54overMALE45_54_2014 <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = whiteMALE45_54overMALE45_54_2014), color = NA) +
  labs(title = "whiteMALE45_54overMALE45_54_2014",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-25,100), high = "purple", low = "yellow",
                    breaks = c(5, 10, 20, 40, 80)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
pointCHANGEpeakWHTmaleEARNERSprev <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = pointCHANGEpeakWHTmaleEARNERSprev), color = NA) +
  labs(title = "pointCHANGEpeakWHTmaleEARNERSprev",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-25,25), high = "purple", low = "yellow",
                    breaks = c(-15, -10, -5, 0, 5, 10, 15)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
whiteMALE45_54overMALE45_54_2019 <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = whiteMALE45_54overMALE45_54_2019), color = NA) +
  labs(title = "whiteMALE45_54overMALE45_54_2019",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-25,100), high = "purple", low = "yellow",
                    breaks = c(5, 10, 20, 40, 80)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
csaWHITEmaleEARN_14 <- ggplot(data = LCSA_SHP10, aes(fill = csaWHITEmaleEARN_14)) +
     labs(title = "csaWHITEmaleEARN_14",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 75) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
csaPOINTchangeWHITEmaleEARN <- ggplot(data = LCSA_SHP10, aes(fill = csaPOINTchangeWHITEmaleEARN)) +
     labs(title = "csaPOINTchangeWHITEmaleEARN",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
csaWHITEmaleEARN_19 <- ggplot(data = LCSA_SHP10, aes(fill = csaWHITEmaleEARN_19)) +
     labs(title = "csaWHITEmaleEARN_19",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 75) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
whiteMALE45_54overMALE45_54_2014
```

```{r}
pointCHANGEpeakWHTmaleEARNERSprev
```

```{r}
whiteMALE45_54overMALE45_54_2019
```

```{r}
csaWHITEmaleEARN_14
```

```{r}
csaPOINTchangeWHITEmaleEARN
```

```{r}
csaWHITEmaleEARN_19
```

```{r}
black0_17over0_17_2014 <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = black0_17over0_17_2014), color = NA) +
  labs(title = "black0_17over0_17_2014",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-25,100), high = "purple", low = "yellow",
                    breaks = c(5, 10, 20, 40, 80)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
pointCHANGEblackYOUTHprev <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = pointCHANGEblackYOUTHprev), color = NA) +
  labs(title = "pointCHANGEblackYOUTHprev",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-12,20), high = "purple", low = "yellow",
                    breaks = c(-10, -5, 0, 5, 10, 15)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
black0_17over0_17_2019 <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = black0_17over0_17_2019), color = NA) +
  labs(title = "black0_17over0_17_2019",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-25,100), high = "purple", low = "yellow",
                    breaks = c(5, 10, 20, 40, 80)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
csaBLACKyouthPREV_14 <- ggplot(data = LCSA_SHP10, aes(fill = csaBLACKyouthPREV_14)) +
     labs(title = "csaBLACKyouthPREV_14",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 25) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
csaPOINTchangeBLACKyouthPREV <- ggplot(data = LCSA_SHP10, aes(fill = csaPOINTchangeBLACKyouthPREV)) +
     labs(title = "csaPOINTchangeBLACKyouthPREV",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
csaBLACKyouthPREV_19 <- ggplot(data = LCSA_SHP10, aes(fill = csaBLACKyouthPREV_19)) +
     labs(title = "csaBLACKyouthPREV_19",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 25) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
black0_17over0_17_2014
```

```{r}
pointCHANGEblackYOUTHprev
```

```{r}
black0_17over0_17_2019
```

```{r}
csaBLACKyouthPREV_14
```
   
```{r}
csaPOINTchangeBLACKyouthPREV
```

```{r}
csaBLACKyouthPREV_19
```

```{r}
asian0_17over0_17_2014 <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = asian0_17over0_17_2014), color = NA) +
  labs(title = "asian0_17over0_17_2014",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-15,40), high = "purple", low = "yellow",
                    breaks = c(0, 5, 10, 15, 20, 25)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
pointCHANGEasianYOUTHprev <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = pointCHANGEasianYOUTHprev), color = NA) +
  labs(title = "pointCHANGEasianYOUTHprev",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-9,11), high = "purple", low = "yellow",
                    breaks = c(-6, -2, 0, 2, 6)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
asian0_17over0_17_2019 <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = asian0_17over0_17_2019), color = NA) +
  labs(title = "asian0_17over0_17_2019",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-15,40), high = "purple", low = "yellow",
                    breaks = c(0, 5, 10, 15, 20, 25)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
csaPOINTchangeASIANyouthPREV <- ggplot(data = LCSA_SHP10, aes(fill = csaPOINTchangeASIANyouthPREV)) +
     labs(title = "csaPOINTchangeASIANyouthPREV",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
csaASIANyouthPREV_14 <- ggplot(data = LCSA_SHP10, aes(fill = csaASIANyouthPREV_14)) +
     labs(title = "csaASIANyouthPREV_14",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 5) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
csaASIANyouthPREV_19 <- ggplot(data = LCSA_SHP10, aes(fill = csaASIANyouthPREV_19)) +
     labs(title = "csaASIANyouthPREV_19",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 5) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
asian0_17over0_17_2014
```

```{r}
pointCHANGEasianYOUTHprev
```

```{r}
asian0_17over0_17_2019
```

```{r}
csaASIANyouthPREV_14  
```

```{r}
csaPOINTchangeASIANyouthPREV
```

```{r}
csaASIANyouthPREV_19
```

```{r}
pop65plusOVERpop_2014 <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = pop65plusOVERpop_2014), color = NA) +
  labs(title = "pop65plusOVERpop_2014",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-10,40), high = "purple", low = "yellow",
                    breaks = c(0, 5, 10, 15, 20, 25)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
pointCHANGEretireePREV <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = pointCHANGEretireePREV), color = NA) +
  labs(title = "pointCHANGEretireePREV",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-10,20), high = "purple", low = "yellow",
                    breaks = c(-2, 0, 2, 5, 10)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
pop65plusOVERpop_2019 <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = pop65plusOVERpop_2019), color = NA) +
  labs(title = "pop65plusOVERpop_2019",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-10,40), high = "purple", low = "yellow",
                    breaks = c(0, 5, 10, 15, 20, 25)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
csa65plusPREV_14 <- ggplot(data = LCSA_SHP10, aes(fill = csa65plusPREV_14)) +
     labs(title = "csa65plusPREV_14",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 15) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
csaPOINTchange65plusPREV <- ggplot(data = LCSA_SHP10, aes(fill = csaPOINTchange65plusPREV)) +
     labs(title = "csaPOINTchange65plusPREV",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
csa65plusPREV_19 <- ggplot(data = LCSA_SHP10, aes(fill = csa65plusPREV_19)) +
     labs(title = "csa65plusPREV_19",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 15) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
pop65plusOVERpop_2014
```

```{r}
pointCHANGEretireePREV
```

```{r}
pop65plusOVERpop_2019
```
  
```{r}
csa65plusPREV_14
```

```{r}
csaPOINTchange65plusPREV
```

```{r}
csa65plusPREV_19
```

##occupants per room

```{r}
OCCroomsMOREthanONEocc_14 <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = OCCroomsMOREthanONEocc_14), color = NA) +
  labs(title = "OCCroomsMOREthanONEocc_14",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-5,15), high = "purple", low = "yellow",
                    breaks = c(0, 2, 4, 6, 8, 10, 12)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
pointCHNGoccRMmoreTHANone <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = pointCHNGoccRMmoreTHANone), color = NA) +
  labs(title = "pointCHNGoccRMmoreTHANone",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-7,15), high = "purple", low = "yellow",
                    breaks = c(-6, -4, -2, 0, 2, 4)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
OCCroomsMOREthanONEocc_19 <- ggplot() +
  geom_sf(data = countiesSHP10, aes(fill = OCCroomsMOREthanONEocc_19), color = NA) +
  labs(title = "OCCroomsMOREthanONEocc_19",
       caption = "U.S. Census Bureau") +
  geom_sf(data = SHP5, fill = NA, color = "darkgray") +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_binned(limits = c(-5,15), high = "purple", low = "yellow",
                    breaks = c(0, 2, 4, 6, 8, 10, 12)) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "darkgray"))
```

```{r}
csaOCCrmMOREthanONE_14 <- ggplot(data = LCSA_SHP10, aes(fill = csaOCCrmMOREthanONE_14)) +
     labs(title = "csaOCCrmMOREthanONE_14",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 5) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
csaPOINTchangeOCCrmMOREthanONE <- ggplot(data = LCSA_SHP10, aes(fill = csaPOINTchangeOCCrmMOREthanONE)) +
     labs(title = "csaPOINTchangeOCCrmMOREthanONE",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 0) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
csaOCCrmMOREthanONE_19 <- ggplot(data = LCSA_SHP10, aes(fill = csaOCCrmMOREthanONE_19)) +
     labs(title = "csaOCCrmMOREthanONE_19",
          caption = "U.S. Census Bureau") +
  geom_sf(color = "lightyellow") +
  theme_void() +
  scale_fill_steps2(n.breaks = 5, low = "darkgreen", mid = "tan", high = "red", midpoint = 5) +
  theme(legend.justification = c(0, 1),
        legend.position = c(.85, .39)) +
  theme(legend.title=element_blank()) +
  theme(panel.background = element_rect(color = FALSE, fill = "lightyellow"))
```

```{r}
OCCroomsMOREthanONEocc_14
```

```{r}
pointCHNGoccRMmoreTHANone
```

```{r}
OCCroomsMOREthanONEocc_19
```
  
```{r}
csaOCCrmMOREthanONE_14
```

```{r}
csaPOINTchangeOCCrmMOREthanONE
```

```{r}
csaOCCrmMOREthanONE_19
```

## k-means and regionalize

## note for later: change scale on county migration maps 